Reactive power optimization is a typical multi-target nonlinear optimization problem, which is complex and difficult to solve. In recent years, many intelligent optimization algorithms are applied to solve the problem. The particle swarm optimization (PSO) algorithm is one of the most typical reactive power optimization intelligent optimization algorithms, while it still needs to be improved because it is easy to fall into local minima. This paper proposes an algorithm of particle swarm optimization with multi-strategy integration (MSI-PSO). Selection operation, phased adjustment of acceleration factor and the dynamic adjustment of inertia weight are introduced to the speed updating formula to balance the local and global search ability of particles. Some particles with poor performance are selected randomly to amend the individual cognitive part in the speed updating formula as social cognition to improve the accuracy and convergence speed of the particle search. Reactive power optimization simulation model is established with a target of minimum loss of the active network and maximum system voltage stability margin. The weighted method, membership function method and Pareto method are used to deal with the multi-objective problem. Simulation on the IEEE30 bus testing system is conducted. The results show that compared with several other improved PSO algorithms and the PSO algorithm based on Pareto optimal solution set, the proposed MSI-PSO algorithm has better performance and can effectively solve the multi-objective reactive power optimization.