Condition Based Maintenance (CBM) is a key technology enabling facility maintenance cost reduction. The CBM approach to maintenance replaces rigid time-based maintenance schedules with the “right maintenance at the right time” identified by real-time equipment health monitoring. This approach creates a new requirement for determining the best time to schedule newly identified critical maintenance actions in light of the real world constraints of available labor and resources. One of the major challenges encountered when attempting to optimize a maintenance schedule is related to the resolution of the many and often complex interdependencies or constraints present throughout the maintenance process. This paper presents a CBM decision support software tool that leverages real-time current and future health condition information to optimize maintenance resources, tasking, and planning in order to maximize the system readiness. Over the past year Impact Technologies, under contract by NAVSEA, has been developing technologies that will provide the necessary decision support tools to address this dynamic maintenance environment. The software scheduling tool utilizes an Open Systems Architecture for Condition-Based Maintenance (OSA-CBM) architecture to facilitate implementation into new or legacy systems. The tool employs a generic maintenance model that accounts for equipment reliability attributes, maintenance task material and labor requirements, system dependencies, and subsystems relationships. The focus of the development has been on Naval Ship maintenance, but the model inputs can be adapted to a variety of applications including power generators, aircraft, ships, and production facilities. The core of the decision support tool is a multi-sweep optimization algorithm that is tuned to the maintenance scheduling problem. The algorithm has been designed to achieve the best computational speed. Benefits and risks of maintenance decisions have been quantified in risk, which can be defined in terms of readiness or financial. The probability and consequence of each system failure are considered in light of the complex system interdependencies, such as dependant and redundant systems, to achieve the best overall system readiness. Novel post-processing steps identify the active solution constraints further enhancing the user’s ability to understand the issues that affect system availability.