Cloud computing involves a large number of shared virtual servers that are accessible from both public and private networks. It has provided scalable and multitenant computing approaches for Infrastructure as a Service, Software as a Service, and Platform as a Service to cloud users on pay-per-use bases. Over the past decades, researchers from different domains such as astronomy, physics, earth science, and bioinformatics have used scientific workflow applications to model many real-world problems in both paralleled and distributed computing environments. However, achieving efficient workflow scheduling is challenging. This is due to the large size of the task set that each workflow application generates. The complex dependencies between these workflows make it difficult to find an optimal solution to workflow scheduling problems within polynomial time. This paper analyzed workflows scheduling problems in cloud and grid computing environment through providing a comprehensive survey based on the state-of-the-art meta-heuristic algorithms. We analyzed the literature from four perspectives, including (i) existing meta-heuristics, (ii) scheduling efficiency, system performance, and execution budget, (iii) scheduling environment and (iv) quality of service performance metrics. Also, we have presented the research gaps and provided future directions for future investigation.