Allocating jobs to heterogeneous machines in grid systems is an important task in computational grid to effectively utilise computational resources. Particle swarm optimisation (PSO) has been recently applied to grid computation scheduling (GCS) problems and shown very promising results as compared to other meta-heuristics in the literature. However, PSO with the traditional position updating mechanism still has problem coping with the discrete nature of GCS. This paper proposed a new updating mechanism for discrete PSO that directly utilise discrete solutions from personal and global best particles. A new local search heuristic has also been proposed to refine solutions found by PSO. The results show that the hybrid PSO is more effective than other existing PSO methods in the literature when tested on two benchmark datasets. The hybrid method is also very efficient, which makes it suitable to deal with large-scale problem instances.