Cloud Computing, as demonstrated by the NIST (National Institute of Standards and Technology), is a method under which a prevalent pool of resources or assets (networks, servers, services, applications, and storage) could be easily retrieved on demand and it can be released. There seems to be a lot of discussion in the industrial and academic world about its description, future, and context of cloud computing. The development of cloud computing has resulted in many benefits for the implementation of scientific workflows. Workflows are widely utilized application models for computer sciences. It defines a series of calculations that allow data analysis in a distributed and systematic manner and has also been effectively used to produce meaningful technological innovations in multiple computational fields. Cloud Infrastructure as a Service (IaaS) provides a simple flexible, scalable, and accessible infrastructure for the implementation of these applications analysis of cost and time by FPA with PEFT ranking shows high cost and Time. In case of GWO with PEFT reduces cost and time significantly. So, the decision of optimization plays an important role in the scheduling of tasks. Through simulation results it has been observed that Grey Wolf Optimization shows significant results in terms of cost and time on all the workflows of the cloud as compare to Flower Pollination Algorithm (FPA) and Genetic Algorithm (GA).