The application of reliability optimization in the system design and development phase affects system availability, maintainability, logistics support requirement and related cost control greatly. In this research, we consider the system reliability optimization problem under different design constraints such as weight, volume, system and subsystem reliability requirements, etc. System reliability can be improved by enhancing component reliability level and/or adding redundant components. Of course, both component reliability and redundancy enhancement will definitely spend a lot of costs and resources. Therefore, there exists a trade-off between these two alternatives for resource-constrained reliability optimization. Many optimization techniques have been used to allocate component reliability level and the number of redundancy individually. Few of them can solve reliability-redundancy allocation problems simultaneously except for some kind of combinations. The proposed model developed in this paper is based on revised H-J pattern search combined with dynamic programming approach using the concept of dominating sequences. We try to solve the system reliability-redundancy allocation problem which can handle more general system configurations which include series- parallel system, element stand-by system, and n-stage mixed series system etc.. Besides general resource capacity constraints, some specific system and subsystem performance constraints such as reliability will also be considered. Through our approach, we can get the optimal allocation of component redundancy number and reliability level to minimize the total system design and development cost A couple of numerical examples have been demonstrated for different system configurations. The information we obtain from those is very helpful to facilitate the system design and development process. The performance comparison between this proposed approach and other reliability- redundancy allocation methods are also discussed and it turns out to be very good result. We show that this proposed approach can fit various problem types and is more realistic, flexible, even can get better results in some aspect than some traditional reliability optimization approaches.