In the recent years, project scheduling and management has been challenging many researchers and has become an intriguing field of study. There are many factors such as personnel, budget, time, materials,etc. that need to be taken into account before running a project. Practical situations have been successfully modeled in mathematical formulations and many problem test cases have been introduced to deal with project scheduling problems. However, having been proved as NP-hard problem, only projects with small numbers of activities can be solved using linear or integer programming approaches while a real project may involve hundreds or even thousands of tasks or activities to be completed. This study proposes an efficient algorithm based on Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to deal with large-scale project scheduling problems, where an activity can be performed in different modes under precedence relations and resource constraints.