To obtain the optimal solutions of the multi-objective differential evolution algorithm, an adaptive multi-objective differential evolution(AMODE) algorithm based on the dynamic parameters adjustment strategies is developed, in which the adaptive adjustment strategies are designed to select the scaling factor and crossover rate. Then, the suitable scaling factor and crossover rate can be calculated in the mutation and crossover processes to balance the local search and the global exploration abilities of the multi-objective differential evolution algorithm. Thus, the integrity and uniformity optimal solutions are able to be obtained in the evolutionary process. The experimental results show that this proposed AMODE algorithm has a better effect to improve the inverted generational distance(IGD) and spacing(SP).