The project scheduling problem deals with scheduling the activities of a project (or multiple projects) under various types of constraints, mainly scarce resources, time, and precedence constraints. Allocating scarce resources among project activities, taking into account all constraints and optimizing various objectives, is an extremely difficult problem. Even the most classical and simplest variant, the resource-constrained project scheduling problem (RCPSP), belongs to the class of NP-hard problems. Project scheduling problems occur in many areas of industry and other real-world situations. Therefore, they have always been an interesting and important topic for research and industry. Over the decades, different variants of resource-constrained project scheduling problems have emerged as different industrial requirements need to be modeled. Accordingly, researchers have proposed various solution strategies to address these problems. In general, we can categorize some of the main solution approaches as heuristic, meta heuristic, hyper-heuristic, hybrid and exact methods. Although researchers have provided outstanding solutions for different variants of project scheduling problems, the optimal solutions for many benchmark problems are still unknown. In this thesis, we present new solution approaches, including exact, meta-heuristic, and hybrid methods for one of the most generalized forms of the project scheduling problem, the resource-constrained multi-project scheduling problem (MRCMPSP). MRCMPSP is a more accurate representation of the real-world environment. We present several new ideas for solving the MRCMPSP, including new local search techniques based on min-conflicts and iterated local search, a constraint programming model, and hybrid methods. Our solution methods have been successfully applied to other variants of the project scheduling problem, such as the resource-constrained multi-mode project scheduling problem (MRCPSP) and the resource-constrained multi-project scheduling problem (RCMPSP). In addition, our innovative meta-heuristic method based on min conflicts and tabu search has been successfully applied to solve the vehicle routing and scheduling problem with delivery and installation of machinery (DIM) recently introduced by the EURO Working Group in Vehicle Routing and Logistics Optimization (VeRoLog) and ORTEC. Our methods were applied on existing benchmark instances for several project scheduling problems, including MRCMPSP, MRCPSP, and RCMPSP. Computational results show that our hybrid methods, which use meta-heuristics and constraint programming, improve the results of state-of-the-art methods for this class of problems and provide many new upper bounds for benchmark instances.