Electrification of passenger vehicles has been viewed by many as a way to significantly reduce carbon emissions, operate vehicles more efficiently, and reduce oil dependence. Due to the potential benefits of electric vehicle (EV), many federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future, EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve the efficiency for the EV transportation system? This research aims to contribute to this field by proposing an analytical model that determines a time-dependent optimal velocity profile for an EV in order to minimize the electricity usage along a chosen route by systematically considering road characteristics and real-time traffic conditions. In particular, the proposed multistage optimal control model uniquely considers the impact of the presence of intersection queues in both temporal and spatial dimensions, which has been ignored in most traditional speed control models even for internal combustion engine vehicles. In addition, to facilitate the real-time operations, an approximation model, which simplifies the optimal speed profile, is further developed to increase the computation efficiency. The testing using the field data collected from a six-intersection signalized arterial corridor shows that the optimal velocity profile can significantly save energy for an EV, and the computational efficiency of the proposed approximation model is suitable for real-time applications.