The majority of Multi-Agent Reinforcement Learning (MARL) implementations aim to optimise systems with respect to a single objective, despite the fact that many real world problems are inherently multi-objective in nature. Research into multi-objective MARL is still in its infancy, and few studies to date have dealt with the issue of credit assignment. Reward shaping has been proposed as a means to address the credit assignment problem in single-objective MARL, however it has been shown to alter the intended goals of the domain if misused, leading to unintended behaviour. Two popular shaping methods are Potential-Based Reward Shaping and di erence rewards, and both have been repeatedly shown to improve learning speed and the quality of joint policies learned by agents in single-objective problems. In this work we discuss the theoretical implications of applying these approaches to multi-objective problems, and evaluate their e cacy using a new multi-objective benchmark domain where the true Pareto optimal system utilities are known. Our work provides the rst empirical evidence that agents using these shaping methodologies can sample true Pareto optimal solutions in multi-objective Stochastic Games.