The Mobile Edge Computing （MEC） network assisted by Unmanned Aerial Vehicles （UAV）demonstrates great potential in emergency response， real-time monitoring， and other fields. However， the efficient operation of MEC network encounters challenges stemming from multiple optimization objectives， such as high energy consumption and high latency. Therefore， a Multi-Objective Evolution with Deep Deterministic Policy Gradient （MOE-DDPG） algorithm for UAV-assisted MEC network optimization is introduced. Firstly， an integrated multi-objective optimization model is established to ensure comprehensive performance of the MEC network by minimizing latency and energy consumption while maximizing the number of completed UAV tasks. Secondly， a bidirectional selection strategy for weight vector and individual matching is proposed to address the difficulty of balancing various objectives in traditional Deep Deterministic Policy Gradient （DDPG） algorithms when dealing with multi-objective optimization problems， thereby significantly enhancing population diversity. Finally， by organically fusing the Multi-Objective Evolution （MOE） algorithm and DDPG algorithm， a novel MOE-DDPG algorithm framework is proposed， which can optimize the overall performance of the MEC network in real time. The experimental results show that the MOE-DDPG algorithm not only significantly improves the distribution and convergence of the Pareto solution set but also effectively reduces energy consumption， latency， and increases the number of completed tasks.