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In this paper, the problem of minimizing overkills and re-probes in wafer probe testing is formulated as a multiobjective optimization problem. Overkill is a measure of good dies that were considered bad and re-probe is an additional manual probe testing to save overkills. The goal is to provide an optimal setting of threshold values for engineers to decide whether to carry out a re-probe after the two times of automatic probe testing. A two-stage algorithm is proposed to take advantage of particle swarm optimization (PSO) and optimal computing budget allocation (OCBA) for solving a good enough setting that minimizes overkills and re-probes within a reasonable computational time. A crude model based on a shorter stochastic simulation with a small number of test wafers is used as a fitness evaluation in a PSO algorithm to select N good enough settings. Then, we proceed with the refined OCBA to search for a good enough setting. The two-stage algorithm is applied to a real semiconductor product, and the threshold values obtained by the proposed algorithm are promising in the aspects of solution quality and computational efficiency. We have also demonstrated the computational efficiency of our algorithm by comparing with the genetic algorithm and evolution strategy.