diff --git a/pypesto/petab/importer.py b/pypesto/petab/importer.py
index 6160247dd..b36e5b488 100644
--- a/pypesto/petab/importer.py
+++ b/pypesto/petab/importer.py
@@ -466,6 +466,21 @@ def create_objective(
             edatas = self.create_edatas(
                 model=model, simulation_conditions=simulation_conditions
             )
+        else:
+            simulation_conditions = pd.DataFrame(
+                [
+                    {
+                        PREEQUILIBRATION_CONDITION_ID: edata.id.split("+")[0]
+                        if "+" in edata.id
+                        else "",
+                        # why is this not CONDITION_SEP ¯\_(ツ)_/¯?
+                        SIMULATION_CONDITION_ID: edata.id.split("+")[1]
+                        if "+" in edata.id
+                        else edata.id,
+                    }
+                    for edata in edatas
+                ]
+            )
 
         parameter_mapping = (
             amici.petab.parameter_mapping.create_parameter_mapping(
diff --git a/test/petab/test_amici_objective.py b/test/petab/test_amici_objective.py
index 00c399f6e..4b59de78e 100644
--- a/test/petab/test_amici_objective.py
+++ b/test/petab/test_amici_objective.py
@@ -130,3 +130,38 @@ def test_preeq_guesses():
     # assert that resetting works
     problem.objective.initialize()
     assert obj.steadystate_guesses["fval"] == np.inf
+
+
+def test_edatas():
+    """
+    Test whether optimization with preequilibration guesses works, asserts
+    that steadystate guesses are written and checks that gradient is still
+    correct with guesses set.
+    """
+    model_name = "Brannmark_JBC2010"
+    importer = pypesto.petab.PetabImporter.from_yaml(
+        os.path.join(models.MODELS_DIR, model_name, model_name + ".yaml")
+    )
+    pars = np.asarray(importer.petab_problem.x_nominal_free_scaled)
+
+    full_objective = importer.create_objective()
+
+    edatas = importer.create_edatas()
+
+    full_result = full_objective(pars, return_dict=True)
+    for pm_full, edata in zip(
+        full_objective.parameter_mapping.parameter_mappings,
+        edatas,
+        strict=True,
+    ):
+        sub_objective = importer.create_objective(edatas=[edata])
+        pm_sub = sub_objective.parameter_mapping.parameter_mappings[0]
+        for var, val in vars(pm_full).items():
+            assert getattr(pm_sub, var) == val, var
+
+        sub_result = sub_objective(pars, return_dict=True)
+        assert sub_result[C.FVAL] == sum(
+            -rdata["llh"]
+            for rdata in full_result[C.RDATAS]
+            if rdata.id == sub_result[C.RDATAS][0].id
+        )