In the optimization of dragonfly algorithm for mass data, due to too many dimensions to be calculated and too many iterative computations, it takes a lot of operation time. However, the distributed algorithm based on Spark can reduce the operation time of big data. In this paper, the DA algorithm is used to conduct parallel computation on Spark distributed computing platform, and the dragonfly population is distributed to each node. The dragonfly individual information in each node is updated in parallel through multiple threads, and then the global optimal solution is shared, so as to improve the operation speed of mass data optimization. Finally, the verification of the simulation test is carried out through four test functions. The verification results show that on the premise of the accuracy ensured, the DA algorithm based on Spark takes the least time in the optimization calculation of mass data