Vehicular Edge Computing (VEC) can deliver low-latency and high-reliability services to vehicle users. In order to address the challenge of frequent changes in vehicle locations and to enhance the integration of MEC and IoV technologies, this paper presents a simulation of a dynamic VEC network, taking into account various task attributes, vehicle mobility patterns, and time delay constraints. The optimization objective aims to identify edge servers that satisfy the delay constraints imposed by the vehicles’ mobility trajectories while minimizing energy consumption during the task offloading procedure. To achieve this goal, we propose a heuristic mobility-aware offloading algorithm (HMAOA), which continuously updates the resource selection and offloading policies to adapt to changes in vehicle locations. Simulation results demonstrate that the proposed algorithm effectively reduces task processing energy consumption, enhances the task completion rate, and adapts well to dynamic environment.