This paper considers freight transport in urban areas using passenger rail networks as an eco-friendly alternative to current predominantly fossil-fueled trucks. It addresses one of the essential problems, called freight rail transport scheduling problem (FRTSP), where the objective is to minimize the total waiting time of the parcels at their departure stations which is equivalent to minimizing the space occupancy at the stations and maximizing the turnover of the freight transport. In this paper, we focus on designing an effective and robust method to solve the FRTSP. For this purpose, a genetic algorithm is proposed whose performances, as it is widely known, highly depend on the length of chromosomes and control parameters. In this genetic algorithm, the length of chromosomes is reduced owing to a mathematical formulation with fewer integer decision variables representing a parcel-to-train assignment. We prove that as long as a parcel-to-train assignment is known, it takes a polynomial time to check whether this assignment can lead to a feasible solution and, if yes, solve the remaining problem by transforming it into computing the longest distances in a digraph. This result makes it possible to only consider parcel-to-train assignment variables in chromosomes, while efficiently determining the values of the other variables in fitness evaluation. In order to achieve robustness regarding all instances, the control parameters of the genetic algorithm are set with Taguchi method. Computational results show that the method developed in this way is very effective. For small-size instances, it yields optimal solutions within considerably shorter computation times than a commonplace optimization solver does. For large-size instances, it outperforms such a solver, not only providing much higher-quality solutions but also consuming much shorter computation times. This method thus meets very well operational requirements where high-quality solutions are expected while computation time is very limited.