Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that is obtain via the evolutionary process. In this paper, we consider how gradient information can be obtained and used in the context of multi-objective optimization problems. The different types of gradient information are used to guide the evolutionary gradient search to solve multi-objective problems. Experimental studies are conducted to analyze and compare the effectiveness of various implementations.