Through vehicular ad hoc networks, fog computing, which is situated between vehicles and the cloud, gives vehicles more processing, networking and storage power. The traditional vehicular networks are added to the fog computing paradigm. This allows us to support more pervasive vehicles, improve communication efficiency, and do away with limitations of conventional vehicular networks. The well-known problems with vehicular fog computing that cause energy waste are latency and delay. To address such issues, in this work, we plan to develop a task allocation system so that we can allocate tasks to the most optimal node such that we can help vehicular fog computing applications get a faster response. Genetic Algorithm and Particle Swarm Optimization Algorithm are the two-optimization algorithm that we have implemented to do a comparison among the outputs of MinMakeSpan, MinTotalCost, Global best array, which specifically tells us about the task allocated to a particular node, and TotalCost, MakeSpan, Optimal Function value of each algorithm in order to determine the optimal algorithm for task allocation