In this study, we investigated different optimization approaches for the resource allocation problem in the preparation of Air Tasking Orders (ATOs) and analyzed their performances. We developed a genetic algorithm with customized encoding, crossover and fitness calculation mechanisms making use of domain knowledge. We also developed an integer programming model, a simple greedy algorithm and a brute-force algorithm for the same problem to assess the performance of the proposed algorithm and demonstrate our contribution to the resource allocation's effectiveness and efficiency. ATOs are designed to meet the objectives of various air combat missions by optimized resource management. Considering combinatorial aspects with dynamic objectives and various constraints, computer support has become essential for the optimization of resource management in air force operations. We developed a novel solution to this real life time critical problem, which is a time-consuming and gain-optimized decision problem. (C) 2012 Elsevier Inc. All rights reserved.