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Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources. This will lead to resources having high workload and stagnation may occur if computational times of the processed jobs are high. This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing. The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max – Min Ant System. The algorithm focuses on local pheromone trail update and the trail limit values. A matrix is used to record the status of the available resources. The agent concept is also integrated in this algorithm for the purpose of updating the grid resource table. Experimental results obtained showed that this is a promising ant colony algorithm for job scheduling in grid environment.