Internet of vehicles (IoV) is gradually combined with connected autonomous vehicles (CAV), which accelerates the development of CAV. In order to meet the service requirements of CAV, mobile edge computing (MEC) provides IoV with a novel paradigm which provides services by fast processing vehicle tasks at the road side units distributed near target vehicles. In this way, vehicle tasks can be offloaded to edge servers deployed in road side units (RSU). A vehicle tasks offloading problem requires load balance of edge servers to be maintained with minimum total time cost. Thus, we proposed a vehicle tasks offloading method (VTO) in which the vehicle tasks offloading problem is formulated as a multi-objective optimization problem. Hence, we design a multi-objective optimization evolutionary algorithm basing on improving the strength pare to evolutionary algorithm (SPEA2) and technique for order preference by similarity to ideal solution (TOPSIS) and multiple criteria decision making (MCDM). Through theoretical analysis and experimental evaluation, the results shows that the performance of VTO is effective and efficient.

车联网（IoV）逐渐与联网自动驾驶汽车（CAV）结合，加速了CAV的发展。为了满足CAV的服务需求，移动边缘计算（MEC）为车联网提供了一种新的范式，通过在分布在目标车辆附近的路边单元快速处理车辆任务来提供服务。通过这种方式，可以将车辆任务卸载到部署在路边单元 (RSU) 中的边缘服务器。车辆任务卸载问题需要以最小的总时间成本维持边缘服务器的负载平衡。因此，我们提出了一种车辆任务卸载方法（VTO），其中车辆任务卸载问题被表述为一个多目标优化问题。因此，我们设计了一种多目标优化进化算法，该算法基于改进进化算法（SPEA2）的强度和通过与理想解的相似性（TOPSIS）和多准则决策（MCDM）的顺序偏好技术。通过理论分析和实验评估，结果表明VTO的性能是有效且高效的。