Focusing on the issue that emergency resource assignment for multiple demand points and multiple supply points in railway emergencies, an emergency resource assignment model of multiple rescue targets was established, which was based on the concept of "soft time window". The maximum fairness and minimum total assignment cost were considered as the optimization objectives, and parallel selected genetic algorithm was used to solve the model. The population was equally divided into subpopulations by the algorithm. Subpopulations' number was equal to the number of objective functions. An objective function was assigned to each divided subpopulation and the selection work was done independently, by which individuals with high fitness were selected from each subpopulation to form a new population. Crossover and mutation were done to generate the next generation of population. The computing cases show that the parallel selected genetic algorithm reduces the variance of resource satisfaction degree of all demand points by 93.88% and 89.88% respectively, and cuts down the cost by 5% and 0.15% respectively, compared with Particle Swarm Optimization (PSO) and two-phase heuristic algorithm. The proposed algorithm can effectively reduce the variance of the resource satisfaction degree of all demand points, that is, it improves the fairness of each demand point and reduces the cost at the same time, and can obtain higher quality solution when solving multiple objective programming problem.