The rapid demand and usage of the Internet of Things (IoT) devices, such as smart watches, vehicles and mobile devices, are generating massive amount of data on daily basis. Cloud computing due to distance from IoT devices is not advisable for processing of massive data especially for delay sensitive applications, e.g., medical applications. Due to far from IoT devices, executing vast data at cloud will create delay issues as well as need more bandwidth. To save bandwidth, reduce delay and efficient processing of time critical applications, fog is appropriate choice but have confined storage. Cloud and fog both are complement to each other, and integration of cloud and fog is suitable platform for wide range of applications, due to huge storage and computing capability of cloud, and low latency characteristics of fog computing. This paper formulates the problem of scheduling of tasks to cloud or fog nodes using hybrid approach: GASPSO (Genetic algorithm and success rate-based particle swarm optimization algorithm) with the aim of minimizing energy and cost. Initially detailed architecture of fog is explained and proceeding further GASPSO is elucidated. Implementation of GASPSO is performed on problem taken in MATLAB 2023a and compared with state-of-the-art algorithms. Results show the effectiveness of given algorithm in achieving stated objective such as energy as well as cost.