In this paper, a resource investment problem is addressed based on employee-timetabling and according to the employee timetabling in practical production systems. A mathematical model aimed at minimizing resource investment is proposed for this problem. In order to solve this problem more efficiently, the mathematical model of the original problem is simplified according to the resource investment and the properties of employee-timetabling constraints. The model proposed is proved to have the same optimal solution as the original mathematical model, and its great advantage in solving speed is verified through the CPLEX software solution process. Meanwhile, for the large-scale problem, as the constraints of employee-timetabling lead to resource occupancy between shifts, it is difficult to obtain a good solution by using the traditional activity list encoding method. A genetic algorithm with a new coding method is designed in this paper, which encodes the job delay time to search the starting time of the job. Moreover, two local optimization methods are proposed which can optimize the delay time and the starting time of jobs to improve the solution obtained by using the genetic algorithm. A comparison of the numerical experiments with CPLEX and the literature demonstrates the validity of the algorithm.