We propose a diagnostic workflow to reveal behavioral variation in multi-objective reinforcement learning policies. The method highlights differences in policy trajectories beyond expected returns, offering quantitative and visual tools for policy inspection. Validated on grid worlds and scaled to continuous control tasks, it effectively captures behavioral diversity under increasing complexity.