AbstractThose days are gone when storing and accessing of data were done on computer’s hard drive. Now with innovation in technology and with the great success of Internet Computing resources have become more economical, more powerful and more ubiquitously available than ever before. This technological trend of the 21st century has given birth to the realization of a new computing model called Cloud Computing. This Computing is not only about the hard drives were storing and accessing can be done but it is latest computing paradigm and it offers tremendous opportunities to solve the large-scale scientific problem. To fully exploit the applications of cloud, various challenges need to be addressed where scheduling is one among them. Although catholic research has been done on Workflow Scheduling, there are very few edges tailored for Cloud environments. For some basic principles of Cloud such as elasticity and heterogeneity existing work fails to meet optimal solution. Therefore our work focuses on the scheduling strategies for scientific workflow on IaaS cloud. We present an algorithm based on the meta- heuristic optimization technique where the best of two algorithms Ant colony Optimization (ACO) and Particle Swarm Optimization (PSO) are merged to optimize locally and globally which minimizes the overall workflow time (makespan) and reduces the cost. Our heuristic is evaluated using CloudSim and several well-known scientific workflows of different sizes. The results show that our approach performs better when compared to PSO algorithm.