A modified differential evolution algorithm with dynamic scaling factor and adaptive mutation operator was proposed in this paper. Dynamic scaling factor which was determined according to population information during the evolutionary process

was introduced for handling the constraint optimization problem; adaptive mutation operator which mutate partial individuals based on population diversity was adapted to overcome premature evolution and enhance the probability of finding global optimum. Polypropylene grade transition optimization model was established and the modified differential evolution algorithm was applied to solve the problem. Experiment results demonstrated that both search ability and efficient were superior to those of the original differential evolution algorithm.