Electric Vehicles (EV) are now a days proposed to serve the electric power grid bi-directionally by means of consuming energy from grid and also by injecting back the captive energy within the EV battery upon grid requirements. Thus EV and its known variants like Battery Electric Vehicles (BEV) and Plug-in Hybrid-Electric Vehicle (PHEV) possess unique rewards compared to the conventional fossil fueled vehicle. The increasing number of EVs integration with electricity network could have a significant knock on the performance and planning of a power system especially in the demand side management. The recent studies made by the National laboratory of U S Department of Energy clearly mentions the risks involved in EV integration in terms of its peak demand profile and spinning reserve profile. The work in this paper investigates the behavior of different types of EVs & its impact on the load profile in a grid connected system in terms of EV capacity, EV charging levels and EV penetration time. The charging profile thus obtained for the above different cases clearly conveys very significant and relevant information regarding its influence on the peak time demand. The peak time period is extended to late hours respective of the different charging conditions which has a definite impact of DSM. Also, an intelligent algorithm is developed to take care of the Demand Side Management (DSM) issues. For the same, the algorithm inputs the grid as well as the vehicle parameters. The uniqueness of the proposed algorithm is in its ability to avoid the communication complexities with the Independent System Operator (ISO) & aggregator. The work is done after studying relevant market models of EVs having different similar or different characteristics.