Infrastructure as a Service (IaaS) is one of the common Cloud service models, which is widely used by the scientific applications. As the users are charged only for the usage of resources based on the Service Level Agreements (SLA), the users are attracted towards the IaaS. Workflow scheduling is a complex issue in IaaS because multiple scheduling parameters are to be considered to satisfy the Quality of Service parameters. The Workflow scheduling in Cloud has to focus on more than one scheduling parameter, in order to provide the optimal schedule for the workflow. In this paper four workflow scheduling algorithms are compared. It comprises of two heuristic algorithms and two meta-heuristic algorithms. All the four algorithms are tested with Regular graphs of scientific workflows such as Montage, Epigenomics, LIGO and SIPHT. The results of the algorithms showed consistent performance. Among the four algorithms, Differential Evolution Algorithm for Workflow Scheduling (DEWS) in Public Cloud, yields optimal results with respect to the scheduling parameters makespan and cost.