This paper presents TREX, a Trajectory-based Explainability framework designed for Multi-Objective Reinforcement Learning (MORL). The work addresses the limitation that traditional Explainable Reinforcement Learning (XRL) methods are typically tailored for single scalar rewards and fail to provide explanations when agents optimize multiple conflicting objectives simultaneously. The proposed approach enables agents to explicitly reason about trade-offs between different objectives and generates interpretable explanations for the decision-making process behind objective trade-offs. By focusing on trajectory-level explanations, TREX provides insights into how agents navigate decision spaces when balancing competing objectives in complex real-world scenarios.