Abstract: Riverbank aquifers are of great importance for water supply sources, and their groundwater dynamics are strongly influenced by pumping and groundwater-surface water interactions. Designing effective groundwater monitoring networks to estimate the groundwater exploitation and invert hydrogeological parameters is one of the core issues in the assessment of the sustainable utilization of regional groundwater resources. In this study, a surrogate model based on the Kriging method for the numerical groundwater model was established for a homogeneous unconfined riverbank aquifer, and Markov chain Monte Carlo method was used to construct an optimal design of the sequential monitoring network with the expectation of the a priori to a posteriori relative entropy of the parameters as the utility function. It enables the joint inversion of the groundwater exploitation and the hydraulic conductivity of riverbed sediment (K_r) in the riverbank aquifer. The case study demonstrates that the surrogate model based on the Kriging method can effectively replace groundwater numerical models, enhancing computational efficiency. The optimized monitoring scheme method based on the proposed method can adequately capture changes in the flow field of the riverbank aquifer and accurately estimate the groundwater exploitation and the parameter K_r. Comparative analyses between the optimized monitoring strategy and random monitoring strategies indicate that the optimized approach achieves superior accuracy in parameter inversion. Comparisons of many sets of optimal monitoring well placement schemes with different layouts show that areas around pumping wells and near rivers with significant changes in the hydraulic gradient are key areas for the placement of monitoring to effectively estimate the groundwater exploitation and invert the K_r. This study provides scientific support for the design of monitoring well networks in riverbank aquifers and offers valuable insights for groundwater resource evaluation and sustainable management.