We investigate the trade off between investing effort in improving the features of a research environment that increases productivity and investing such effort in actually conducting the research experiments using a less elaborated, albeit sufficiently operational environment. The study case presented is an interactive genetic algorithm environment we created to evolve user interfaces designs. We present three productivity improvements integrated in our environment and examine whether on the long run the research productivity can be in fact increased by spending development time on enhancing the research tools rather than on performing the research itself. The three improvements are the integration of the entire system interface into a main wxPython window, the addition of a runs manager for setting up multiple experiments, and the creation of a data manager for effective exploration and visualization of data produced in the experiment runs. We also discuss several guidelines for transitioning a research environment such as ours from a researcher?s tool to an end-user?s tool.