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In the process of Hardware/software co-design of spacecraft control computers, the multi-objective optimization is a key problem. The current strength Pareto evolutionary algorithm has some advantages in solving high-dimensional multi-objective optimization problems, but the computing time-complexity during the step of environmental selection is still very large. Aiming at this point, an improved algorithm was proposed. With the finite K-nearest neighbor method, new algorithm reduces the number of comparisons to lower the time-complexity from O(M³) down to O(max(l,logM)M². The experimental results show that the proposed algorithm not only improves the running speed, but also acquires better convergence and distribution diversity than the original one. [ABSTRACT FROM AUTHOR]