The brain storm optimization (BSO) algorithm is a new type of swarm intelligence optimization algorithm, which is inspired by the brainstorm problemsolving model and is suitable for solving complex multimodal function optimization problems. However, when solving multimodal extremum, the BSO requires iterative operations. When computing large data sets, its computation efficiency and accuracy are too low. In order to solve the above problems, we design and implement a parallel brainstorm optimization algorithm based on Spark. By parallelizing the clustering with the highest computational complexity in the BSO algorithm and the generation process of new populations, the speedup and efficiency of the algorithm are improved. In particular, based on the idea of parallelization, the population is divided into multiple subgroups for co evolution, and each subgroup produces new populations to maintain the diversity of the population and improve the convergence speed of the algorithm. Finally, the parallel BSO algorithm is used to solve the multimodal function. Experiments show that, when the total number of cores of the parallel nodes is 10, the computation time of the parallel BSO algorithm is saved by 50%, the computational accuracy is basically equal to the serial BSO algorithm, and the convergence speed is improved obviously. The results prove the validity of the parallelization of the BSO.