Actual multi-objective optimization problems change with time or environments(called as dynamic multi-objective optimization problems, DMOPs), thus detection of environmental change and algorithm response are two key steps to solve DMOPs during the full-cycle optimization. This paper focuses on summarizing the research on the latter one, i.e., dynamic multi-objective evolutionary algorithms(DMOEAs). To solve DMOPs effectively, a large number of DMOEAs with good tracking performance have been proposed in the past two decades. However, few literatures analyse and report the existing studies from the spatiotemporal perspective. Therefore, this paper introduces review of research on DMOEAs from this view. First, the basic concepts, DMOPs, and performance indicators are introduced. Then, the research on DMOEAs proposed in the past five years are introduced from the spatiotemporal view. Finally, some current challenges exist in DMOEAs are given, and future studies are prospected.