Many scientific disciplines require processing computational-intensive applications with vast quantities of data. Large scale systems, such as Grid, provide shared resources on geographically distributed heterogeneous computing systems to meet the computational demands of the applications. One of the fundamental problem computing with the Grid system is assigning tasks of various users to the resources efficiently such that the execution time of the application is minimized while Grid resources are utilized optimally. This is called the task matching problem, an NP-hard problem, the focus of this thesis. While there are many heuristics for the task matching problem, particle swarm optimization (PSO) has been one of the latest heuristics in the literature adopted as a solution to this problem. However, PSO has few drawbacks, such as premature convergence to local optima. To circumvent these problems, we incorporate a learning approach inspired by the genetic algorithm to guide the PSO algorithm. We propose three novel genetic learning PSO algorithms: modified genetic learning PSO (MGLPSO), hybrid genetic learning PSO (HGLPSO), and modified genetic learning PSO with adaptive mutation (μ-MGLPSO). The proposed algorithms have been designed for both single and multi-objective task matching problem for tasks with and without dependencies. The proposed techniques are evaluated on standard benchmarks with large data sets and compared against state-of-the-art algorithms.