Air cargo is vital for efficient global supply chains, enabling the rapid transport of valuable goods. Combination airlines, a significant player in the industry, use both dedicated full freighters and the belly space of scheduled passenger flights to transport cargo. This research develops a decision tool that combination airlines can use to optimize the routing and scheduling of full freighters. By incorporating anticipated cargo demand data as an input, the tool enables the most cost-effective routing and scheduling of full freighters, also taking into account the available cargo carrying capacity offered by passenger flights. The tool combines and leverages the intrinsic advantages of passenger flights (frequencies) and full-freighters (capacity). The decision tool relies on a Mixed Integer Linear Program (MILP) to minimize operational costs while adhering to operational constraints faced by a combination airline. Although this method performs well for small instances, it suffers from long computational times when applied to larger problem instances. To address this, modifications are introduced to shorten the runtime by reducing the problem size. Specifically, clustering is used to group cargo requests together, and column generation is used to efficiently select optimal routing decisions. These modifications lead to a shorter runtime, albeit with an acceptable increase in operating costs. The decision tool serves multiple purposes. In the short term, it can be used to route full freighters and identify non-profitable full-freighter origin-destination pairs, among other applications. Additionally, it can be used to analyze, simulate, and provide recommendations for future long term full freighter fleet planning decisions.