Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation, it is possible to establish an inverse model based on monitoring well data to identify a set of contamination source parameters including source intensity (M), release location (X₀,Y₀) and release time (T₀). To address the issues of insufficient monitoring data from the wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an example with an instantaneous release of a contaminant in a confined groundwater aquifer was employed. Under the condition of single well monitoring and determined monitoring counts, with the target of optimization of monitoring location D and monitoring frequency Dt, both the single-objective monitoring scheme with the minimum information entropy of the model parameter posterior distribution and the multi-objective monitoring scheme with the smallest information entropy and the shortest monitoring time were optimized respectively. According to the optimized monitoring scheme, the delayed rejection adaptive Metropolis algorithm was used to identify the pollution source parameters. The case study results showed that under the condition of pre-set single well monitoring and 5monitoring times, the single-objective optimized monitoring scheme was D=(830.2,199.8),△t=2.7. Under this monitoring scheme, the mean errors of inverse 4pollution source parameters M,X₀,Y₀,T₀ were 19.5%, 13.2%, 3.4%, and 1.3%, respectively. The multi-objective optimization monitoring scheme was D=(807.9,199.4),△t=1.2. Under the monitoring scheme, the mean errors of inverse 4parameters M,X₀,Y₀,T₀ were 19.9%, 13.4%, 3.7%, and 4.2%, respectively. Compared with the monitoring scheme based on the single-objective optimization, while the inversion mean error of the pollution source parameters based on the multi-objective optimization monitoring scheme increased by 0.4%, 0.2%, 0.3%, 2.9% respectively, the monitoring time has been significantly reduced by 55.6%.