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In order to reduce the energy consumption of the central air-conditioning system, and aim at the problem of the optimal load allocation in multiple chiller system, a prediction model of energy efficiency in chillers is proposed. Based on random forest feature optimization combined with kernel function extreme learning machine, the model improves the prediction accuracy by eliminating redundant features. Then an improved tunicate swarm algorithm based on hybrid strategy(ITSA)is proposed. Firstly, whale spiral search strategy is coalesced to improve individual update methods. Secondly a non-linear dynamic weights is introduced to balance global exploration and local development. Thirdly somersault strategies are used to avoid falling into partial optimal. Finally, on the basis of the energy-efficiency model, ITSA is used to optimize load allocation of multiple chiller system. The experimental results show that the random forest feature optimization can effectively improve the accuracy of the energy efficiency prediction model. ITSA can effectively exert the energy saving potential of the system by optimizing the on-off status and load ratio of the chillers. Compared with the original method, the energy consumption can be reduced by about 6%, which shows that the method is appropriate for optimal load allocation of multiple chiller system.