One of the main aspects of the production industry is the optimization of acceptance SAMPLING plans. SAMPLING plan performance depends on many uncertain factors that are difficult to model, especially in a multi-stage process. Therefore, it requires an innovative procedure to optimize it. This study presents an innovative DOUBLE SAMPLING plan for a multi-stage process based on discrete event simulation (DES) toward proposing an applicable plan to the inspection of the product whose accepting probability follows the hypergeometric distribution for a finite lot without replacement. This paper focuses on the five economic parameters of a DOUBLE SAMPLING plan that are determined by minimizing the average sample number (ASN) with the help of DES results and optimization methods. Several experiments based on DES were tested to determine the regression function of the ASN simulation study was carried out using Enterprise Dynamic software (ED). Our economic statistical lot acceptance SAMPLING plan based on minimizing the average sample number has been developed to determine the acceptance parameters including the first acceptance number, first rejection, number, first sample size, second sample size, and second acceptance number in a multi-stage process. According to all runs of the simulation model, we concluded at a 95% level of confidence that ASN ranges from 530.16 to 554.93, which is given in detail in the paper.