The electric vehicle (EV) industry has been rapidly developing internationally due to a confluence of factors, such as government support, industry shifts, and private consumer demand. Envisioning for the future connected vehicles, the popularity of EVs will have to handle a massive information exchange for charging demand. This inevitably brings much concern on network traffic overhead, information processing, security, etc. Data analytics could enable the move from Internet of EVs to optimized EV charging in smart transportation. In this paper, a mobile edge computing (MEC) supporting architecture along with an intelligent EV charging recommendation strategy is designed. The global controller behaves as a centralized cloud server to facilitate analytics from charging stations (CSs) (service providers) and charging reservation of on-the-move EVs (mobile clients) to predict the charging availability of CSs. Besides, road side units behave as MEC servers to help with the dissemination of the CSs’ charging availability to EVs, and collecting their charging reservations, as well as operating decentralized computing on reservations mining and aggregation. Evaluation results show the features of the MEC-based charging recommendation system in terms of communication efficiency (low cost for information dissemination and collection) and improvement of charging performance (reduced charging waiting time and increased fully charged EVs).