Mutation-based fuzzing is one of the most popular vulnerability discovery solutions. Its performance of generating interesting test cases highly depends on the mutation scheduling strategies. However, existing fuzzers usually follow a specific distribution to select mutation operators, which is inefficient in finding vulnerabilities on general pro- grams. Thus, in this paper, we present a novel mutation scheduling scheme MOPT, which enables mutation-based fuzzers to discover vulnerabilities more efficiently. MOPT utilizes a customized Particle Swarm Optimization (PSO) algorithm to find the optimal selection probability distribution of operators with respect to fuzzing effectiveness, and provides a pacemaker fuzzing mode to accelerate the convergence speed of PSO. We applied MOPT to the state- of-the-art fuzzers AFL, AFLFast and VUzzer, and implemented MOPT-AFL, -AFLFast and -VUzzer respectively, and then evaluated them on 13 real world open-source pro- grams. The results showed that, MOPT-AFL could find 170% more security vulnerabilities and 350% more crashes than AFL. MOPT-AFLFast and MOPT-VUzzer also outperform their counterparts. Furthermore, the extensive evaluation also showed that MOPT provides a good rationality, compatibility and steadiness, while introducing negligible costs.