Biologically detailed models are essential tools in neuroscience, and automated methods are frequently used to build and validate such models based on experimental data. The open-source parameter optimization software Neuroptimus was developed to enable easy application of advanced parameter optimization methods, such as evolutionary algorithms and swarm intelligence, to various problems in neuronal modeling. Neuroptimus includes a graphical user interface, and works on various platforms, including PCs and supercomputers. While Neuroptimus uses various built-in cost functions and the eFEL feature extraction library to compare model behavior to experimental data, it severely limits the range of neuronal behaviors that can be targeted by optimization. However, the popular model-testing framework SciUnit allows for the implementation of tests that quantitatively evaluate arbitrary model behaviors. Therefore, the goal of this project is to extend Neuroptimus to use HippoUnit, an open-source neuronal test suite based on SciUnit, to evaluate model performance during optimization. This project aims to develop a seamless integration between HippoUnit and Neuroptimus, enabling the construction of detailed biophysical models of hippocampal neurons. The new Neuroptimus-HippoUnit integration will allow the optimization of a broader range of neuronal behaviors, which can ultimately lead to improved biophysical models of hippocampal neurons.