In the past several decades, significant amount of resources have been spent by many manufactures for product quality improvement. Quality improvement efforts involve mitigating the impact of manufacturing variation through robust design, statistical process control (SPC), and inspection. This research focuses on manufacturing system design issues related to inspection. The problem of determining the optimal inspection strategy for a given multi-stage manufacturing system i.e. inspection strategy that will result in the lowest total processing cost is modeled as a joint optimization of inspection location and type. Three inspection options considered in this work are no inspection, full inspection and sampling inspection. This thesis presents a generic mathematical model and employs dynamic programming to identify the optimal inspection plan with minimum total processing cost. Numerical examples are presented to describe the solution procedure. The conclusions are supported by a factorial experiment. A sensitivity analysis is also conducted to gauge the impact of inspection errors on the inspection strategy. The developed model is extensible and applicable to solving manufacturing and inspection allocation problems.