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Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES) is a black-box optimization method useful for applications where no direct inversion is possible. We present the development of a parallel CMA-ES algorithm that reduces the runtime for a specific geophysical data analysis, dipole localization. We compare our parallel algorithm against several other parallel CMA-ES variants on a sample dataset for dipole localization. We improve the performance of CMA-ES for the problem of finding dipoles in a subsurface environment as part of a closed-loop near-real-time wireless bioremediation system, REACTS (near-REal-time Autonomous bioremediation of ConTamination in the Subsurface). The goal of the performance improvement is to enable near-real-time analysis of geophysical data. For this application, our algorithm shows significant performance improvement over the other variants.