Electric Vehicles (EVs) have economic and environmental benefits for owner and the community. However, EV fleet charging may affect distribution network (DN) in negative manner. In order to overcome this problem, charging process should be coordinated well. If the charge coordination is inadequate to satisfy network standard, that can be provided by injecting power from some of the available EVs to grid. The concept, where EVs supply power to the network, is called Vehicle-to-Grid (V2G) and efficiency, reliability and stability of the network can be improved with V2G technology. Disadvantages of V2G concept are cost of coordination, infrastructure changes, battery degradation and disruption of EV owner comfort. In this paper, some most popular heuristic algorithms such as Genetic Algorithm (GA), Partical Swarm Optimization (PSO), Differential Evaluation (DE), and Artificial Bee Colony (ABC) are used in order to optimize the charge/discharge coordination in V2G concept. The optimization algorithms decide status of each EV to minimize the coordination cost considering network and EV constraints. Thus charging processes of EVs are affected as less as possible from coordination process. Results show that, all the given algorithms satisfy the network requirements and GA is the best in terms of optimization performance.