The specification and placement of Green Infrastructure (GI) features for urban stormwater management is a challenging task because of the potentially large number of decision variables and the multi-objective nature of the decision problem. To address this challenge, a handful of numerical hydrologic-model-based optimization tools have been developed. However, most tools support a limited set of search algorithms that have not been thoroughly evaluated, which suggest the possibility of substantial computational improvement and/or better performance in finding global optimum management solutions. We explored the application of a new open-source multi-objective optimization tool that couples the Storm Water Management Model (SWMM) with the Optimization Software Toolkit for Research Involving Computational Heuristics (OSTRICH). OSTRICH-SWMM (Macro et al., 2019) supports multiple search algorithms and exploits distributed computing resources using parallelized search algorithms that can in turn invoke parallelized versions of SWMM, thus facilitating the analysis of applications that incorporate highly detailed SWMM models and a complex multi-objective decision framework. This computational flexibility raises some challenging questions about the best way to leverage available high-performance computing assets (e.g. thousands of cores distributed across hundreds of nodes). A case study based on the recently updated Buffalo Sewer Authority’s city-wide SWMM model facilitates an exploration of these issues and also demonstrates the overall capability and flexibility of OSTRICH-SWMM for a variety of applications.