This dissertation details how smart charging (SC) techniques can be employed to aid or accelerate the convergence of electric vehicles (EVs) and the power grid, in light of current challenges thereof. As the market penetration of EVs rises across vehicle segments, the corresponding increase in EV charging load is expected to significantly stress electric power distribution infrastructure, resulting in diminished power quality. Therefore, there exists a need to manage the grid impacts of EV charging. However, management is complicated by the possibility of competing interests between EV owners (including private individuals and fleet operators) and grid operators. For example, EV owners may prefer minimum-cost or minimum-time EV charging, which might conflict with a grid operator's preference to minimize peak charging loads. This dissertation introduces `two-stage’ SC, which is an optimization-based procedure for planning the charging behavior (i.e., power-versus-time trajectories) of one or more EVs. Two-stage SC is designed to simultaneously satisfy the interests of competing stakeholders (e.g., EV owner and grid operators) by capitalizing upon the existence of multiple optimal (or near-optimal) behaviors that will satisfy one stakeholder (e.g., the EV owner). This dissertation contains (i) multiple two-stage SC algorithms (for residential and commercial EV charging scenarios), (ii) detailed assessments of the ramifications of two-stage SC (with particular emphasis on grid impacts), and (iii) demonstrations of two-stage SC in practice. Taken together, the results of this dissertation show that simple, decentralized decision-making can be employed to effectively mitigate the grid impacts of EV charging with little-to-no compromise in benefits to EV owners. Consequently, this work represents a set of practical techniques to accelerate the convergence of EVs and the power grid.