<p>The fast growth in the field of Internet of Things has brought the development of many safety services and applications in the world transportation system. Tasks generated by these vehicular applications are resource intensive and needs to be processed on the fly. To satisfy the computation requirements of these applications, tasks generated by the applications are offloaded to the promising Vehicular Edge Computing (VEC) network for processing. Time sensitivity of vehicular applications and the high mobility of the vehicles are the major challenges in VEC that necessitates the need for efficient offloading and processing strategy. In this paper, a hybrid meta-heuristic algorithm for tasks offloading in VEC network is proposed. First, the tasks offloading problem in VEC is modeled as a multi-objective model considering task completion time and transmission time as QoS parameters. Secondly, a hybrid meta-heuristic algorithm based on Simulated Annealing (SA) algorithm and Red Deer Algorithm (RD) is proposed to generate an optimal tasks offloading schedule that minimize the system completion time (makespan time) and transmission time (latency). The RDA is employed for global exploration of the search space and the Simulated Annealing (SA) algorithm is employed to perform local search to enhance the solution generated by the red deer algorithm. The solution generated by the RDA at the previous step is updated with the best found solution either by the SA algorithm or the RDA algorithm based on probability function. The proposed approach was simulated using MATLAB and the performance of the RDSA is compared with the state-of-the-art meta-heuristic approaches used in tasks scheduling and offloading. The results of the simulation prove that the proposed RDSA algorithm can significantly reduce the overall system completion time and transmission time respectively.</p>