The goal of this research is providing optimization approach for bi-objective scheduling work shifts and job rotation problem in order to exploit efficient performance of employees. In this article scheduling work shifts and job rotation problems had been modeled in one mathematical model with two objective functions: minimizing labor cost and maximizing number of job rotation. Also human factors (fatigue, learning and forgetting) that have effect on workers performance were modeled. Presented model was mixed integer and nonlinear and genetic algorithm and ε-constraint technique have been used to solve it and gain Pareto sets. To illustrate efficiency of provided algorithm, its performance has been compared to results of LINGO. The results indicated that performance of genetic algorithm is better than that of LINGO in terms of computational time and objective value. To relate relationship between objectives, set of problems have been solved. Obtained Pareto indicated that there is a conflict between objectives. Hence with considering human factors that have effect on workforce’s performance it is needed to plan work shifts and job rotation simultaneously. Results indicated that the proposed optimization approach is capable to provide suitable alternatives while managers try to consider decreasing cost, increasing jobs variety and multi-skilled training.