The rapid development of transportation electrification has increased the popularity of interconnected power-transportation systems (IPTSs). However, the increasing frequency and uncertainty of natural hazards, such as typhoons, pose threats and risks of potential damage to the operation of IPTSs. Electric vehicles (EVs) can serve as mobile energy sources whose proper scheduling in transportation networks can provide power support for the damaged power networks caused by natural hazards, thus enhancing the system’s resilience. In view of that, this paper proposes a two-stage scenario-based scheduling framework using EVs for IPTS restoration under natural hazard risks. In the first stage of the proposed framework, EVs are pre-allocated and pre-charged at the charging stations to maximize their support potential against the predicted hazards. In the second stage, EVs are re-dispatched to restore the damaged IPTS. In addition, to represent the damage and reduce the computational burden, this study introduces a scenario generation approach that indicates the real hazard’s impact on the IPTS, which is followed by a scenario-reduction algorithm. To verify the effectiveness of the proposed model, four typical modes are set in the experiment. The results indicate that the proposed IPTS model based on the IEEE 33-bus distribution network and the Sioux Falls transportation network can outperform the benchmark by 367.9%. Additional larger-scale numerical experiments and computational cost analysis based on the IEEE 123-bus power network are conducted to validate the scalability of the proposed method.