In this study, we developed two repetitive types of sampling plans for processes with multiple quality characteristics based on the overall yield index S^T_pk. These plans can be implemented for mutually independent and normally distributed characteristics. The plans are optimally designed based on the asymptotic sampling distribution of S^T_pk using an efficient nonlinear optimization algorithm. During the solution of optimization problems, the average sample number required for inspection and the contract requirements are treated as an objective function and constraints, respectively. The optimal parameters were determined for use in industrial environments with various combinations of requirements in tables. A simulation study was also conducted to show that the tabulated parameters based on the results obtained by large sample theory can guarantee the specified risks for finite sample sizes. Moreover, the limitations of the proposed plans with respect to the sample size were analyzed based on extensive simulations. Numerical calculations and graphical illustrations are presented to demonstrate the properties of the proposed plans. In addition, the advantages of the schemes are discussed compared with existing plans. Finally, the efficient plan is applied to two real industrial problems.