In real world, there are many dynamic multi-objective optimization problems. In the field of evolutionary algorithms, there is a rapid increase in research work dealing with dynamic multi-objective optimization problems. However, dynamic multi-objective test functions play an important role in measuring algorithms performance. This paper proposes a set of new dynamic multi-objective test functions to evaluate the performance of algorithms based on the existing dynamic multi-objective test functions. The difficulty of dynamic multi-objective test functions is affected by shapes of Pareto-optimal solutions and Pareto-optimal front. According to this idea, a set of test functions is designed by introducing the change shape of Pareto-optimal solutions and combining with the existing shift mode of Pareto-optimal solutions. Three algorithms are tested on the proposed test functions. The simulation results show that the designed functions bring challenges to the algorithms and reveal the advantages and disadvantages of the algorithms.