The process plan selection and job shop scheduling are carried out separately and sequentially in many factories and the scheduling is always conducted after the process plan of each job has been determined. In fact, the activities for the determination of process plan and the scheduling plan are coupled with each other and actually complementary. Implementation of the two activities with an appropriate collaborative approach is essential to achieve greater performance and higher productivity for the manufacturing system. In this article, a novel cross-entropy-based approach for the joint process plan selection and scheduling optimization that can assist process planning and scheduling system to achieve optimal scheduling plan and determine the operations, machine for each operation and operation sequence for each job collaboratively was proposed. In order to facilitate the manipulation and improve the optimized performance of the approach, an efficient representation scheme and a generation method for samples were developed. Meanwhile, the updating mechanism for new introduced probability distribution parameters according to which the cross-entropy procedure generates samples was established. To verify the adaptability and performance of the proposed approach, experimental studies were conducted and comparisons were made between this approach and some previous methods. The experimental results indicate that the proposed approach is an alternative and acceptable method to solve the joint process plan selection and scheduling optimization problem.