Grid computing is a high performance distributed computing platform to solve complex and large-scale scientific problems. It consists of heterogeneous computing resources connected by a network across dynamic and geographically distributed organisations to create a distributed high performance computing infrastructure. Job scheduling in computational Grid is known as NP-complete problem owing to the problem complexity and intractable nature of the problem. Such a problem could be solved using heuristic algorithms. These types of algorithms have the ability to find a near optimal solution in reasonable time rather than the optimal solution in a very long processing time. The primary objective of the scheduling is to minimise the makespan of the system. In this paper, an Improved Cuckoo Search (ICS) optimisation method has been proposed for scheduling user-jobs to available resources so that various performance metrics are optimised. Here, our attention has been focused on the improvement of computational Grid performance in terms of makespan and completion time. The study reveals that the proposed ICS algorithm provides better results in comparison with Cuckoo Search (CS) and Simulated Annealing (SA).