In order to form a standardized combat process and combat outline, the multi-objective decision-making problem of killing chain based on particle swarm optimization is studied by directly using the network to allocate real killing chain tasks. On the basis of the traditional kill network model, the combat links are classified in detail, and the auxiliary combat links are introduced into the kill chain according to the specific combat requirements. A multi-objective kill chain evaluation system with damage, timeliness, economy and invulnerability as objective functions is designed. The improved particle swarm optimization algorithm is used to solve the model. The algorithm position update strategy, external archive update and fusion sorting strategy are improved and optimized, and verified by multi-objective standard examples and simulated battlefield data. The results show that the algorithm has a better optimization level on the standard example compared with other algorithms, and is effective and practical in the multi-objective decision-making of battlefield kill chain.