AutoDock is a widely used automated protein docking program in structure-based drug-design. Different search algorithms, such as Simulated Annealing, traditional Genetic Algorithm and Lamarckian Genetic Algorithm are used in AutoDock. However, the docking performance is still limited by the local optima issue in simulated annealing or the premature convergence issue existing in traditional evolutionary algorithms (EA). Because of the stochastic nature of the search algorithms, usually users need to do multiple runs to get reasonable docking results, which is time-consuming. We have developed a new docking algorithm AutoDockX by applying a sustainable GA named ALPS to the protein docking problem. We tested the docking performance over three different proteins (pr, cox and hsp90) with more than 20 candidate ligands for each protein. Our experiments showed that the sustainable GA based AutodockX gives significantly better docking performance than all the existing search algorithms implemented in the latest version of AutoDock4. Our algorithm has the benefits of less running time and much higher robustness. Instead of running a genetic algorithm or LGA search, many times (e.g. 10), a single run of AutoDockX allows us to get better results. AutodockX thus has unique advantages in large-scale drug-candidate virtual screening.