In statistical quality control, usually the mean and variance of a manufacturing process are monitored jointly by two statistical control charts, e.g., a X-bar charts and a R or S chart. As today’s manufacturing firms are moving towards agile manufacturing, quick and economic on-line statistical process control solutions are in high demand. Multiple sampling X-bar charts and multiple sampling S charts are such an alternative. Because of the efficiency of double sampling (DS) and triple sampling (TS) X-bar charts in detecting shifts in process mean and DS and TS S charts in process standard deviation it seems reasonable to investigate the joint DS and TS X-bar and S charts for statistical quality control. In this thesis based on the designs of joint double sampling X-bar and S charts, joint triple sampling X-bar and S control charts are formulated and solved with a genetic algorithm. The efficiency of the TS charts is compared with that of the Ds charts. The results of the comparison show that TS charts are more efficient in terms of average run length (ARL) without increase sampling.