diff --git a/docs/source/rst_doc_files/classes/sampling/sampling.rst b/docs/source/rst_doc_files/classes/sampling/sampling.rst
index 168777e5..96e46baa 100644
--- a/docs/source/rst_doc_files/classes/sampling/sampling.rst
+++ b/docs/source/rst_doc_files/classes/sampling/sampling.rst
@@ -21,9 +21,6 @@ The function should return the samples (``input_data``) in one of the following
 * A :class:`~pandas.DataFrame` object
 * A :class:`~numpy.ndarray` object
 
-.. note::
-   
-   ...
 
 
 .. _implemented samplers:
@@ -36,7 +33,6 @@ To use the sampler in the data-driven process, you should pass the function to t
 .. code-block:: python
 
    from f3dasm.design import ExperimentData, Domain
-   from 
 
    domain = Domain(...)
    # Create the ExperimentData object
@@ -63,4 +59,5 @@ Name                     Method
 ``"random"``             Random Uniform sampling                                                `numpy.random.uniform <https://numpy.org/doc/stable/reference/random/generated/numpy.random.uniform.html>`_
 ``"latin"``              Latin Hypercube sampling                                               `SALib.latin <https://salib.readthedocs.io/en/latest/api/SALib.sample.html?highlight=latin%20hypercube#SALib.sample.latin.sample>`_
 ``"sobol"``              Sobol Sequence sampling                                                `SALib.sobol_sequence <https://salib.readthedocs.io/en/latest/api/SALib.sample.html?highlight=sobol%20sequence#SALib.sample.sobol_sequence.sample>`_
+``"grid"``               Grid Search sampling                                                   `itertools.product <https://docs.python.org/3/library/itertools.html#itertools.product>`_
 ======================== ====================================================================== ===========================================================================================================
diff --git a/src/f3dasm/_src/design/samplers.py b/src/f3dasm/_src/design/samplers.py
index 8d9468d0..05a78410 100644
--- a/src/f3dasm/_src/design/samplers.py
+++ b/src/f3dasm/_src/design/samplers.py
@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 # Standard
+from itertools import product
 from typing import Optional
 
 # Third-party
@@ -39,6 +40,9 @@ def _sampler_factory(sampler: str, domain: Domain) -> Sampler:
     elif sampler.lower() == 'sobol':
         return SobolSequence(domain)
 
+    elif sampler.lower() == 'grid':
+        return GridSampler(domain)
+
     else:
         raise KeyError(f"Sampler {sampler} not found!"
                        f"Available built-in samplers are: 'random',"
@@ -283,3 +287,56 @@ def sample_continuous(self, numsamples: int) -> np.ndarray:
         # stretch samples
         samples = self._stretch_samples(samples)
         return samples
+
+
+class GridSampler(Sampler):
+    """Sampling via Grid Sampling
+
+    All the combination of the discrete and categorical parameters are
+    sampled. The argument number_of_samples is ignored.
+    Notes
+    -----
+    This sampler is at the moment only applicable for
+    discrete and categorical parameters.
+
+    """
+
+    def get_samples(self, numsamples: Optional[int] = None) -> pd.DataFrame:
+        """Receive samples of the search space
+
+        Parameters
+        ----------
+        numsamples
+            number of samples
+
+        Returns
+        -------
+            Data objects with the samples
+        """
+
+        self.set_seed(self.seed)
+
+        # If numsamples is None, take the object attribute number_of_samples
+        if numsamples is None:
+            numsamples = self.number_of_samples
+
+        continuous = self.domain.get_continuous_parameters()
+
+        if continuous:
+            raise ValueError("Grid sampling is only possible for domains \
+                             strictly with only discrete and \
+                            categorical parameters")
+
+        discrete = self.domain.get_discrete_parameters()
+        categorical = self.domain.get_categorical_parameters()
+
+        _iterdict = {}
+
+        for k, v in categorical.items():
+            _iterdict[k] = v.categories
+
+        for k, v, in discrete.items():
+            _iterdict[k] = range(v.lower_bound, v.upper_bound+1)
+
+        return pd.DataFrame(list(product(*_iterdict.values())),
+                            columns=_iterdict)
diff --git a/src/f3dasm/_src/experimentdata/experimentdata.py b/src/f3dasm/_src/experimentdata/experimentdata.py
index 0963f97d..fb49bb80 100644
--- a/src/f3dasm/_src/experimentdata/experimentdata.py
+++ b/src/f3dasm/_src/experimentdata/experimentdata.py
@@ -277,8 +277,9 @@ def from_sampling(cls, sampler: Sampler | str, domain: Domain | DictConfig,
 
         Parameters
         ----------
-        sampler : Sampler
-            Sampler object containing the sampling strategy.
+        sampler : Sampler | str
+            Sampler object containing the sampling strategy or one of the
+            built-in sampler names.
         domain : Domain | DictConfig
             Domain object containing the domain of the experiment or hydra
             DictConfig object containing the configuration.
@@ -291,6 +292,17 @@ def from_sampling(cls, sampler: Sampler | str, domain: Domain | DictConfig,
         -------
         ExperimentData
             ExperimentData object containing the sampled data.
+
+        Note
+        ----
+
+        If a string is passed for the sampler argument, it should be one
+        of the built-in samplers:
+
+        * 'random' : Random sampling
+        * 'latin' : Latin Hypercube Sampling
+        * 'sobol' : Sobol Sequence Sampling
+        * 'grid' : Grid Search Sampling
         """
         experimentdata = cls(domain=domain)
         experimentdata.sample(sampler=sampler, n_samples=n_samples, seed=seed)
@@ -1359,6 +1371,7 @@ def sample(self, sampler: Sampler | str, n_samples: int = 1,
         * 'random' : Random sampling
         * 'latin' : Latin Hypercube Sampling
         * 'sobol' : Sobol Sequence Sampling
+        * 'grid' : Grid Search Sampling
 
         Raises
         ------