The digital reactor protection system (RPS) is one of the most important digital instrumentation and control (I&C) systems utilized in nuclear power plants (NPPs). It ensures a safe reactor trip when the safety-re-lated parameters violate the operational limits and condi-tions of the reactor. Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings. The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP, based on a Bayesian Belief Network (BBN) model. The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components. Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive anal-ysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system. A reliability framework clarified as a reliability block diagram transformed into a BBN repre-sentation was constructed for each architecture to identify which one will fit the required reliability. The results showed that the highest availability obtained using the proposed method was 0.9999998. There are 120 experi-ments using two common component importance measures that are applied to define the impact of I&C modules, which revealed that some modules are more risky thanothers and have a larger effect on the failure of the digital RPS.