Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on demand access to shared resources over the Internet in a self-service, dynamically scalable and metered manner. To reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map the job to resources that optimize more than one objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes long time to find an optimal solution. In cloud environment, it is best to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey on optimization algorithms for cloud environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and a novel technique: League Championship Algorithm (LCA).