Abstract: Energy-efficient path planning has been proved to be a feasible approach to improve the energy efficiency of an automated guided vehicle (AGV) and promote the energy saving of a manufacturing workshop. Current research on AGV energy consumption (EC) optimization from the perspective of path planning mainly focuses on a single transport task, and rarely involves multiple transport tasks. To fill the gap, with a single load AGV as the research object, an energy-efficient AGV path planning model is established, which is oriented to multiple transport tasks and takes transport distance and EC as the optimization objectives. Then, a two-stage solution method is proposed. In the first stage, the energy-efficient AGV path planning is executed for the load transport stage associated with each transport task, and the possible no-load transport stages, to obtain the corresponding optimal paths. In the second stage, the non-dominated sorting genetic algorithm-Ⅱ is adopted to determine the optimal transport task execution order and select the best transport path for each transport stage. Finally, the case study verifies the energy-saving effect of the presented model.