Dynamic optimization problems in chemical processes are often quite challenging because these problems often involve multiple and conflicting objectives. To solve the multi-objective dynamic optimization problems (MDOPs), in this paper, we propose a new multi-objective differential evolution (MODE) variant, named MODE-RMO for short, inspired by the phenomenon that good individuals which contain good information often have more chance to be utilized to guide other individuals. In MODE-RMO, the ranking-based mutation operator is integrated into the MODE algorithm to accelerate the convergence speed, and thus enhance the performance. The performance of our proposed algorithm is firstly evaluated in ten test functions and compared with other MOEAs. The results demonstrate that MODE-RMO can generate Pareto optimal fronts with satisfactory convergence and diversity. Finally, MODE-RMO is applied to solve three MDOPs taken from literature using control vector parameterization. The obtained results indicate that MODE-RMO is an effective and efficient approach for MDOPs.