The primary objective of a defense aviation operator or air force, is to maximize its continued readiness to successfully perform military flight operations, whenever and wherever the home state or international community calls for it. In order to maintain a satisfactory level of operational readiness, air forces need to ensure that sufficient aircraft are mission capable and continue in this state for an adequate period of time. Furthermore, a specified amount of training hours need to be produced to keep all aircrew in mission capable condition. These requirements must be fulfilled at all time, which requires an involved planning process. Since aircraft are subject to stringent safety requirements, preventive maintenance must be performed at prescribed flight time intervals, which causes downtime and affects operational readiness. As a result, all preventive maintenance efforts as well as the flight assignments should be planned and scheduled adequately for the entire aircraft fleet. This process called ‘flight and maintenance planning’ (FMP) is highly complex and time consuming due to numerous constraints. Furthermore, deviations from the schedule are inevitable due to various uncertainties. Hence, the scheduling process should be responsive, flexible and fast. However, in practice, aircraft utilization tends to be managed manually and on a day-to-day basis, leading to a reactive and overly time-consuming approach, in which problems with respect to operational readiness and controllability can easily develop. Besides, long-term utilization of (preventive maintenance governed) aircraft components is uncontrolled, which results in fluctuating demand for resources and may affect readiness on the aircraft level. To solve these shortcomings, this research introduces two interconnected novel mathematical optimization methods for flight and maintenance planning on both aircraft and component level. The developed methods take into account all relevant requirements and constraints from industry practice and address all aspects of operational readiness, aiming for a pro-active, efficient and more robust scheduling effort. Both models are implemented for real historical problem instances drawn from the Royal Netherlands Air Force (RNLAF) CH-47D Chinook transport helicopter fleet. Subsequently, the generated outputs are compared with the actual output of the RNLAF for the exact same problem instances as a means of validation and demonstration of the model’s performance. The aircraft FMP model results demonstrate that fleet operational readiness can be increased by up to 22% for the subject years, while using the same organizational resources, and fulfilling the same operational requirements. Furthermore, the model supports in rapid generation of feasible long-term flight and maintenance plans, which strongly improves flexibility, anticipation and control. The results of the component FMP model demonstrate that proactive scheduling of substitutions between aircraft and spare inventory, can be utilized towards increased overall component serviceability. This reduces the variability on demand for preventive maintenance, which leads to budgetary and logistical benefits. The developed methods provide a comprehensive and versatile FMP solution for defense aviation operators and provide a strong foundation for development of more complex or comprehensive FMP models.