In the ever-evolving landscape of smart city applications and intelligent transport systems, vehicular edge computing emerged as a game-changing technology. Imagine a world where computational resources are no longer restricted to distant cloud servers but are brought nearer to the vehicles and users. Task offloading enables the computation in edge and cloud server. This proximity not only minimizes network latency but also enables a unfold of vehicles to process tasks at the edge, offering a swift and interactive response to the scenarios of applications with delay sensitivity. To deal with this constraint, an integrated methodology is utilized to enhance the offloading process. The proposed system integrates the particle swarm optimization (PSO) and genetic algorithm (GA). The integrated system optimizes task allocation by exploring the solution space effectively and ensuring efficient resource utilization while minimizing latency. In the evaluation, PSO+GA exhibits enhanced adaptability to varying task sizes, facilitating efficient offloading to the edge as needed. Energy efficiency varies between the algorithms, with PSO+GA generally showing minimal energy consumption. When compared to already existing algorithms such as Energy aware offloading, no offloading and random offloading, PSO+GA outperformed these algorithms in system performance and less energy consumption by a factor of 1.18.