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In the actual industrial process, the predictive control algorithm is widely used, but the parameters of the multivariable predictive control algorithm are more and the parameters are coupled with each other, so tuning the parameters are complicated. In view of this, a predictive control parameters optimization algorithm based on improved particle swarm optimization is proposed. The basic idea of this algorithm is that the mechanism of parasitic behavior is introduced into the particle swarm optimization algorithm to form a two-population particle swarm optimization algorithm, the improved particle swarm algorithm is used to off-line optimization the parameters of the multivariable predictive control algorithm, so as to determine the optimal values of predictive control algorithm parameters. Finally, the algorithm is used to control the liquid level and temperature of a cold and hot water system, and the algorithm is compared with the standard particle swarm optimization algorithm through simulation. The simulation results show that the proposed algorithm is used to optimize the parameters of multivariable predictive control, the step response of system has advantages such as good anti-jamming performance, little overshoot, short adjusting time and so on.