Maintenance for highway bridges is crucial in order to keep the network in a satisfactory condi-tion for users but is also a costly affair. This paper proposes a dynamic, Bayesian network-based model to provide cost-efficient strategies in the context of bridge network management. Characteristics related to un-certainties in both the degradation phase and subsequent maintenance strategies are handled through the de-sirable probabilistic dependencies properties BNs possess. The extension to a specific version of Influence di-agrams allows formulating the optimization part of the problem in order to eventually provide long-term strategies as well as minimize expected costs. To that end, a case study that tackles both conditional and un-conditional cases is presented.