An advanced planning and scheduling systems is defined as any computer program that uses advanced mathematical algorithms to perform optimization on finite capacity scheduling, sourcing, resource planning and others. The expectations of APS systems have been high, both from academia and industry in the subject area of manufacturing planning and control. A common assumption in APS problems is that the processing time of a given product is constant and independent of its position in the production sequence. However, the real processing time of each job on a machine depends on the position of that job in the sequence and its operator’s skill that could be boosted during working time, which is known as learning effect. In order to get closer to the actual conditions of the APS problems, in this paper, a mixed optimization model for a multi-product APS is proposed. The main novelty of the paper is proposing a more efficient mathematical model for the problem of integrating planning and scheduling with learning effect. As this model classified as a NP-Hard problem, a meta-heuristic method, multi-stage genetic algorithm solution, is presented. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution methods.