To overcome the insufficiency of premature and trapped in a local optimum which existed in the calibration optimization of accelerometer based on particle swarm optimization(PSO),a hybrid PSO algorithm based on differential evolution was proposed by dual populations parallel evolutionary and information sharing. It was applied to rapid calibration of accelerometer. In order to improve the optimization ability of hybrid algorithm, a weighted mutation operator was proposed which could balance global exploration and local development ability of DE algorithm, and the nonlinear properties of the Logistic function was introduced to dynamically adjust the inertia weight of PSO algorithm and the weighted coefficient of DE algorithm. Benchmark functions simulation showed that the proposed hybrid algorithm in convergence speed, convergence precision, global search performance and robustness were better than PSO and DE algorithm. And the calibration results of accelerometer showed that the hybrid algorithm could effectively improve the calibration precision of the accelerometer.