Efforts to learn more about the oceans that surround us have increased dramatically as the technological ability to do so grows. Autonomous Underwater Vehicles (AUVs) are one such technological advance. They allow for rapid deployment and can gather data quickly in places and ways that traditional measurement systems (bouys, profilers, etc.) cannot. A ROMS-based data assimilation method was developed that intelligently plans for and integrates AUV measurements with the goal of minimizing model standard deviation. An algorithm developed for this system is first described that optimizes paths for AUVs that seeks to improve the model by gathering data in high-interest locations. This algorithm and its effect on the ocean model are tested by comparing the results of missions made with the algorithm and missions created by hand. The results of the experiments demonstrate that the system is successful in improving the ROMS ocean model. Also shown are results comparing optimized missions and unoptimized missions.