In order to optimize and improve path planning, bacterial evolutionary algorithm and multi-objective decision theory were combined. The vehicle overload was considered during angle encoding; During creation of initial population, the initial angle was decentralized selected to avoid premature convergence; Through mutation and gene transfer, the number of population was remarkably reduced and the evolution speed was improved: With the survival probability, path length and turning angle were used to comprehensively evaluate the flight path, the optimal path was selected based on multi-objective decision theory. The experimental result reveals that the planned path can avoid threat effectively, and the optimization efficiency is improved significantly.