The projected and current adoption rates of electric vehicles are increasing. Since electric vehicles require that they be recharged continually over time, the energy needs to support them is immense and growing. Given existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. The infrastructure for the charging stations has to also be resilient and reliable to absorb this increase in load. Charging stations therefore should be included in the plans for post power disruption planning. In this work, two two-stage stochastic programming models are developed that can be used to determine a power grid expansion plan that sup- ports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. Another three-stage stochastic programming model is presented, where the decisions are made first to select which charging stations to install and expand uninterruptible power supply units and renewable energy sources. Then, when the disrup- tion occurs in the second-stage, repairs in power system and charging stations take place ahead of the arrival of panicked population to prepare for the expected surge in power de- mand. Finally, as demand is unveiled, managerial and operational decisions at the charging stations are made in the third-stage. To solve the mathematical models, we utilize hybrid approaches which mainly make use of Sample Average Approximation and Progressive Hedging algorithm. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.