Abstarct: Optimization of truck- shovel allocation in open pit mines is including finding optimal routes for the transportation of materials, carrying amount of material from any of the stations and allocation of real-time truck to each of the points of loading and unloading, so that maximum utilization of equipment to be. For large mines with several loading stations that may be more than one type of mineral extraction, models and limitations of the truck – shovel flexible allocation becomes more complicated, so that operational research techniques that may be incapable of solving these models. Therefore in this study of genetic algorithm as one of the following branches of artificial intelligence that has had wide application and successful in solving optimization problems, for solving the allocation problem is that too many restrictions, is used. Special focus of this research on one of the largest mines in Iran that is Sungun copper mine. For this purpose, the study of various models proposed for the allocation of dispatch, the model presented by Li has been selected for this mine. This model also had shortcomings that in this study resolve these shortcomings and model developed with experimental collected data for a real shift from the sungun mine was solved using genetic algorithms. To compare the results of genetic algorithm, the model was solved using the LINGO software. The results indicate good performance of genetic algorithms to solve allocation models, so that also meets all the restrictions in the allocation model. The results of applying the flexible allocation model developed by Li for Sungun copper mine represents an increase of 27.5 percentage Shift production toward the traditional allocation method is available.