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A hybrid multi-objective optimization algorithm based on partial aspects of evolution strategy combining stochastic and deterministic elements with the aim of a high efficiency and high scalability suitable for massively distributed finite element analysis is investigated. The selection and generation process of solution candidates depend on density in design variable and objective space. New solution candidates are generated by stochastic variation but also systematically using a triangular grid. With respect to effective design analysis an approximation method is used. The proposed algorithm is evaluated using test cases and is successfully applied to dimension and shape optimization of a permanent magnet synchronous motor.