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Aiming at the problem of low positioning accuracy caused by ranging error in Wireless Sensor Networks, a hybrid particle swarm optimization and differential evolution algorithm (HPSO-DE) for node localization is proposed. Firstly, the inertia weight of PSO is updated adaptively to improve its global exploring ability, so that each individual increases with the iterations, thereby improving its global exploration ability, and then improving the mutation strategy of the differential evolution algorithm to improve the locality of the algorithm. After the individual is optimized by the improved particle swarm optimization algorithm, and the individuals below the average fitness value continue to be optimized by the improved differential evolution algorithm to obtain the HPSO-DE algorithm. The HPSO-DE algorithm inherits the advantages of both, and improves the optimal solution precision and convergence speed of the algorithm. Finally, the HPSO-DE algorithm is applied to the wireless sensor network node location model. The simulation results show that the proposed HPSO-DE algorithm has a positioning error of 2.1m and 1.1m less than PSO and DFOA, respectively, when the ranging error is 30%, and has a high positioning accuracy and greater resistance to errors.