An important factor in an efficient operation of freight railroad companies is their ability to obtain routes and schedules that improve rail network capacity utilization. In this paper we present a decision tool to aid train planners obtain quickly good quality routes and schedules for short time horizons (e.g., daily) to better manage the limited track capacity available for train movements. This decision tool is made up of an integer programming (IP)-based capacity management model and a genetic algorithm (GA)-based solution procedure. The capacity management model assigns trains to routes based on the statistical expectation of running times in order to balance the railroad traffic. The GA procedure determines the best initial routes and release times for trains to depart from origin stations and enter a network, given travel time estimates across aggregated sections of a network. We test our modeling technique by comparing the travel times obtained for a network in Los Angeles using these initial routes and release times, with those obtained from a simulation model, presented by Lu et al. (2004), which has been shown to be representative of the real-world travel times. Our experimental results show that our recommended solution procedure is capable of lowering travel times, as estimated by Lu et al. (2004), by up to 20%.