According to the problem that better dimensions information of particles will loss in Quantum-behaved Particle Swarm Optimization (QPSO) algorithm when solving multi-dimensions problems, a strategy with crossover operator was introduced and the quality of solutions and the performance of algorithm would be improved. Firstly, the whole update and evaluation strategy on solutions in algorithm was analyzed and the better dimensions information of particles would loss because of the mutual interference between dimensions. Secondly, when the evolution was executed dimension by dimension, the algorithm complexity would increase exponentially. Finally, multi-crossover method was employed to increase the retaining probability of excellent dimension information. The comparison and analysis results of the proposed method, with linearly decreased coefficient control method and non-linearly decreased coefficient control method on 12 CEC2005 benchmark functions were given. The simulation results show the modified algorithm can greatly improve the QPSO performance compared with the basic QPSO in 10 functions and also get better performance in 7 functions compared with the other two QPSO variants. Therefore, the proposed method can improve the performance of QPSO effectively.