Utilizing small aerial vehicles (UAVs) to implement coordinated strike against ground targets is a hot application in the current combat, and how to quickly and effectively carry out multi-target allocation is an important prerequisite for the task planning. Firstly, a multi-objective optimization model is established in the battlefield environment considering payload constrains and task requirements in this paper. Secondly, an improved ant colony optimization (ACO) algorithm is designed to solve this problem. Lastly, two different mission scenarios are used as case studies for algorithm performance comparisons and simulation experiments of coordinated strike target allocation. The simulation results show that the improved ACO algorithm converges faster and performs better in larger scale tasks.