When dealing with complex multimodal function optimization problems, the particle swarm optimization(PSO) algorithm trends to trap in local extreme. Aiming at the problem that it is hard for the PSO algorithm to meet the requirements of maritime moving target search, a factorial-based particle swarm optimization algorithm is presented, which combines swarm intelligence with factorial experiment design and employs the ideas of randomization and blocking. By designing multi combinations of parameters with different values and calculating the fitness value of each combination, the fitness curves of each parameter can be obtained. This algorithm analyzes the impact of each parameter on the fitness and the interaction between different parameters. Based on that, the shapes of solution spaces can be acquired, and the population can employ different strategies for different parameters to search for the global optima. For the parameters with significant interaction, the population will focus on global search. For the parameters with insignificant interaction, the population will focus on local search. Finally, this paper applies the proposed algorithm to solve maritime moving target search problems. The experimental results show that compared with other algorithms, the proposed algorithm can generate better search plans effectively.