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Two new algorithms are presented for multi-objective optimization of mixed-variable, stochastic systems. Both are based off of prior algorithms, but combine those pre-exsting algorithms with several other methods, to include single-objective formulations, surrogates, n-dimensional visualizations, aspiration an reservation levels, and direct search methods. Results are shown for a test set of 13 problems, ranging from 2 to 8 objectives, and including non-convex, mixed-variable, and discontinuous problems.