One of the first steps in setting up an optimal groundwater remediation design problem is developing an appropriate objective function, which represents the primary goals of the design. Selecting appropriate objective functions can be challenging. A more realistic objective function, which is a cost function applied to a realistic site without simplification, may yield more accurate results but at the same time it will require more time and effort to develop the appropriate function for a particular application. On the other hand, a simple function will save setup time but may sacrifice the accuracy of the results. This research seeks to identify what situations encountered in remediation design would make the development of a realistic objective function necessary. It also examines tradeoffs among three objectives: total cost, risk, and total cleanup time. A pump-and-treat system is designed for a case study to explore these questions. The model used here is NSGA II (Non-dominated Sorting Genetic Algorithm-II) combined with two numerical models (Modflow and RT3D) and an exposure and risk assessment model. Four different cost functions are applied, ranging from simple to complex. The results show that the realistic cost function generally found better solutions than the simplified ones, especially for shorter-term cleanups. These findings are now being tested for a field-scale application at Umatilla Army Depot in Oregon.