The standard particle filter has long problems referred as sample degeneracy and impoverishment. Requiring large number of samples to achieve suitable estimation accuracy, which reduces the comprehensive performance of the algorithm. This paper proposes a hybrid multi-strategy optimization particle filter method based on Levy flight strategy, differential evolution and success-history strategy. The Levy flight strategy enricifies the basic framework of the sample set, ineffective particles with low-weight are optimized through the differential evolution algorithm, and successful history strategy is used to adjust the parameters to achieve a balance between the global search and the local search, so as to prevent particles from falling into the local optimum when the motion scale is too large. Experiments show that the proposed algorithm can effectively improve the particle diversity, accuracy and sample degeneracy under low measurement noise, reducing the number of particles needed to estimate nonlinear systems.