Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function. Each reward function is defined using a set of objectives that are scaled via a unique combination of weights to balance guiding the spacecraft to the target mission orbit, incentivizing faster flight times, and penalizing propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usage, flight time, and state discontinuities from a reference trajectory for each control scheme. The resulting low-thrust trajectories are used to examine a subset of the multi-objective trade space for the SmallSat trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.