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To improve the diagnosis accuracy of refrigeration system faults, an optimized Least Squares Support Vector Machine (LSSVM) based fault diagnosis method using the improved gravity search algorithm (IGSA) was proposed. Firstly, to increase the memory and information sharing ability of the particles, the gravitational search algorithm was further developed using the speed updating mechanism in particle swarm optimization algorithm, so that its calculation convergence and the search accuracy were improved. Through optimizing the kernel parameters and regularization parameters of LSSVM using IGSA, the proposed IGSA-LSSVM fault diagnosis model was then developed. Finally, using the experimental data obtained from a real refrigeration system, four typical faults of the refrigeration system were successfully identified by the optimized IGSA-LSSVM model. The fault diagnosis results, in comparison with that using the standard LSSVM, GSA-LSSVM and PSO-LSSVM models, showed that the proposed IGSA-LSSVM method was better as expressed in terms of its identification ability and generalization ability.