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The uncertainty of physical parameters in earth system models has a huge impact on the performance of climate simulations. Tuning physical parameters is critical to improving the accuracy of climate predictions. Usually, in the parameter optimization of earth system model, there are multiple objectives that need to be optimized simultaneously. However, the commonly used multi-objective evolutionary algorithms require a very high computational cost for tuning earth system models. Therefore, this paper proposes a multi-objective parameter optimization method MO-ANN based on multi-layer perceptron(MLP) neural network and surrogate model. This method uses a multi-layer perceptron to build a surrogate model to improve the accuracy and convergence of multi-objective optimization. Comparative experiments on complex mathematical functions and single-column atmospheric models show that the MO-ANN optimization algorithm has obvious advantages over the evolutionary multi-objective algorithms. With the warm pool-International Cloud Experiment(TWP-ICE) single column atmospheric model, the convergence rate of the proposed multi-objective optimization method can be improved by more than 5 times compared with the known NSGAIII method.