<p>This paper explores the task allocation problem for heterogeneous unmanned aerial vehicle (UAV) coalitions in coordinated attacks against dynamic targets. The primary objectives are to minimize task completion time and maximize coalition effectiveness. To create a more realistic mission scenario, multiple constraints, such as resource requirement and task time, are considered. First, a novel UAV coalition model based on resource requirement constraints is proposed, and the coalition optimization problem is formulated as a non-convex mixed integer quadratic programming model to rationally allocate resources, reduce task time, and enhance UAV cooperation. Then, a multi-genotype genetic algorithm (MGGA) with customized crossover and mutation operators is proposed. It ensures that the aforementioned constraints are satisfied, and efficient task allocation is achieved. This algorithm features a specialized coding strategy to prevent the occurrence of a chromosome deadlock condition caused by sub-coalition coupling, in which several UAVs become stuck in an infinite waiting state. Simulation results demonstrate that the MGGA effectively optimizes task allocation for heterogeneous UAV coalitions while maintaining overall coalition performance. Monte Carlo experiments confirm the superior performance of our approach compared to conventional methods.</p>