Abstract: There are more nurses organized in the sudden outbreak of major epidemics, traditional artificial nurses take a long time to schedule, the results of the shift may lead to fatigue of the nurse's body, increasing the risk of medical treatment, and the unevenness of the nurse's duty induces the negative antiepidemic phenomenon of nurse, it is suggested that the problem of nurse scheduling during the major epidemic period boils down to the five hard confinements proposed in this paper as the optimization target, and the seven hard confinements as the constraints of the multi-objective planning model, which can avoid the nurses from working for too long and too concentrated, as little as possible to disturb the nurse's biological clock and reduce the risk of medical malpractice; At the same time, it also considers the even distribution of each class type among nurses, takes into account the mental health of nurses, and avoids the negative anti-epidemic phenomenon caused by nurses' dissatisfaction; The algorithm for solving the multi-objective optimization model of the nurse schedule adopts a two-layer particle swarm genetic algorithm, because the solution obtained by the particle swarm algorithm is most likely a local optimal solution, and embedding the genetic algorithm in particle swarm algorithm is conducive to global optimization and greatly improves the probability of obtaining the local optimal solution. The two-layer coding technology can obtain the nurse number and its corresponding scheduling status at the same time, saving the iteration time of the algorithm; The model and algorithm proposed in this paper were applied to the practical problem of nurse scheduling in Chongqing Three Gorges Central Hospital, which could quickly and efficiently obtain the balanced nurse scheduling table during the epidemic, improve the efficiency of antiepidemic work, and solve the long-term optimal scheduling problem plagued the nurse in the hospital. The nurse scheduling model and algorithm can also be generalized to intelligent scheduling of aviation crew and intelligent scheduling of school classes, which has broad application prospects.