As cloud computing makes the use of services and various resources easier for the user, there has been a growing need for efficient and quick resource utilization and services to enhance the performance and that is what fog computing comes in. Fog computing is an extension of cloud computing that takes place at the network's edge. Simply described, it is a hybrid combination of cloud computing and the Internet of things. It is a highly virtualized platform that connects end devices to standard cloud servers and delivers computation, storage, and networking services. Various task scheduling algorithms are used to schedule the tasks at fog nodes that increase the performance of the system. Task scheduling algorithms are classified into stochastic, deterministic, and hybrid algorithms. Our work depicts a performance evaluation of three population-based optimization algorithms which are used for workflow scheduling in a combination of cloud and fog environment. The following algorithms were used to perform the comparison: particle swarm optimization (PSO), genetic algorithm (GA), and a hybrid combination of PSO and GA. The evaluation function consists of three parameters: makespan, cost, and energy. Also, the performance of these algorithms is analyzed by varying the cloud nodes and fog nodes by keeping the number of end devices fixed. The simulator used for the comparative evaluation is the recently proposed FogWorkflowSim. The research proves that hybrid algorithm of the PSO-GA algorithm performs better than the traditional PSO and GA algorithms in terms of cost and makespan. In future, performance will be evaluated by increasing the number of tasks and more workflows would be added to evaluate the performance. The algorithms would be evaluated against the deadline for workflows.