Hybrid electric propulsive systems (HEPSs) attract increasing research interest due to their environmental and economic merits. However, the design optimization of HEPSs with the single objective of fuel saving may result in increased greenhouse gas (GHG) emission and high cost. The present study proposes a multi-objective optimization method to obtain an optimal trade-off with respect to fuel consumption, GHG emission, and lifecycle cost. Due to high convergence in solving constrained multi-objective optimization problems, the non-dominated sorting genetic algorithm II (NSGA-II) is developed to explore an optimal design space. Performance tests are conducted on a real-time hardware-in-the-loop (HIL) platform. The hybrid diesel/battery/shore power system on an anchor handling tug supply vessel is considered as a study case. The results of the proposed NSGA-II are compared with those from a single-objective optimization pursuing minimum fuel consumption. The proposed method outputs designs that can significantly reduce GHG emission and lifecycle cost by sacrificing low fuel consumption when compared with that of single objective optimization. Furthermore, the HEPS designed by the proposed method exhibits advantages over the conventional propulsive system in terms of all the three aspects.