Of primary importance to the efficient operation and profitability of an airline is adherence to its flight schedule. This paper examines that segment of air traffic control, termed traffic management adviser (TMA), which is charged with the complex task of scheduling arriving aircraft to available runways in a manner that minimizes delays and satisfies safety constraints. In particular, we investigate the effectiveness and efficiency of using genetic search methods to support the scheduling decisions made by TMA. Four different genetic search methods are tested on TMA problems suggested by recent work at the NASA Ames Research Center. For problems of realistic size, optimal or near-optimal assignments of aircraft to runways are achieved in real time. Scope and purpose This paper reports on the application of genetic search algorithms to solve certain complexities associated with air traffic control. Air traffic control is an important practical problem that is difficult to solve by other methods because of non-convex, non-linear, or non-analytic characteristics. Four genetic search algorithms are applied, with consistent advantage being demonstrated by an algorithm based on genetic programming functions. Good results are achieved, with evidence that solutions can be achieved in real time.