In order to cope with the increasing threat of the ballistic missile (BM) in a shorter reaction time, the shooting policy of the layered defense system needs to be optimized. The main decision-making problem of shooting optimization is how to choose the next BM which needs to be shot according to the previous engagements and results, thus maximizing the expected ret of BMs killed or minimizing the cost of BMs penetration. Motivated by this, this study aims to determine an optimal shooting policy for a two-layer missile defense (TLMD) system. This paper considers a scenario in which the TLMD system wishes to shoot at a collection of BMs one at a time, and to maximize the ret obtained from BMs killed before the system demise. To provide a policy analysis tool, this paper develops a general model for shooting decision-making, the shooting engagements can be described as a discounted reward Markov decision process. The index shooting policy is a strategy that can effectively balance the shooting rets and the risk that the defense mission fails, and the goal is to maximize the ret obtained from BMs killed before the system demise. The numerical results show that the index policy is better than a range of competi-tors, especially the mean rets and the mean killing BM number.