In the process of identifying groundwater pollution sources，a monitoring well optimization method based on Bayesian formula and information entropy is proposed for the problem that the monitoring value of monitoring wells is insufficient or the correlation between monitoring values and model parameters is weak. The two-dimensional groundwater contaminant transport model was numerically solved by GMS software. To reduce the computational load of the numerical model repeatedly in the optimization design of the monitoring wells and the identification process of the pollution source, the Kriging method was used to establish the surrogate model of the numerical model. As an optimization index, the optimal monitoring schemes of different monitoring types were selected, and the monitoring cost and inversion accuracy were taken as reference factors for the corresponding monitoring schemes. Then, the differential evolution adaptive Metropolis algorithm was used to identify the pollution source. The case study results show that: The determination coefficient of the Kriging surrogate models of the 7 monitoring wells was greater than 0.98, which indicated that the Kriging surrogate models can well replace the original numerical model. The scheme 1（single well No. 3） based on the lowest monitoring cost has an information entropy of 12.772；The scheme 2 （the combination of well No.2 and No.3） taking the monitoring cost and inversion accuracy into account has an information entropy of 9.723；The scheme 3（the combination of well No.2，3 and 5） with higher inversion precision has an information entropy of 9.377. Both the posterior distribution ranges and the standard deviation of model parameters from scheme 1 to scheme 3 were gradually reduced, which verifies that the information entropy is an effective measure of the uncertainty of the posterior distribution of the parameters.