This study aims to optimize the time to reach the target for Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) by moving in a coordinated manner in an environment full of obstacles. Based on a Pareto-based approach, energy consumption, time to reach the target, and obstacle avoidance processes are optimized. While energy consumption varies according to speed and distance, the time to get to the target is calculated in time steps, and obstacle avoidance performance is calculated using a logarithmic model. For UAVs, energy consumption and obstacle coverage performance were optimized. For the UGVs, energy consumption and obstacle avoidance are balanced. Real-time route optimization is achieved by sharing data between the UAV and the UGV. With this study, obstacle detection mechanisms are strengthened, and mission success is increased in complex and variable environmental conditions. The results show that the proposed method provides optimum performance in terms of obstacle avoidance, energy consumption, and mission duration even in complex environmental conditions. With this structure, the limits of traditional single-objective optimization approaches have been overcome. The proposed method enables the efficient operation of individual and collaborative vehicles. In addition, the adaptive architecture of the system contributes to flexibility and scalability.