In this study, a mixed integer linear programming (MILP) model is developed for rescheduling elective patients upon the arrival of emergency patients by considering two types of clinical units, namely operating rooms and post-anesthesia care units (PACUs). The model considers the overtime cost of the operating rooms and/or the PACUs, the cost of postponing or preponing elective surgeries, and the cost of turning down the emergency patients. The results indicate that a mainstream commercial solver can efficiently find an optimal solution in a particular scenario with light elective surgery load, but becomes very inefficient in searching optimal solutions in all other scenarios. As such, a genetic algorithm is developed to efficiently obtain the approximately optimal solutions in those scenarios that are difficult for the commercial solver. In the genetic algorithm, a novel chromosome structure is proposed and applied to represent the feasible solutions to the MILP model. It is shown that for the scenarios with heavy load of elective surgeries, the genetic algorithm can find approximate optimal solutions significantly faster than the commercial solver. In practice, the two solution methodologies should be used jointly to provide hospitals a solid tool for making sound and timely decisions in admitting emergency patients and rescheduling elective patients