While meta-heuristics are effective for solving large-scale combinatorial optimization problems, they result from time-consuming trial-and-error algorithm design tailored to specific problems. For this reason, a software tool for rapid prototyping of algorithms would save considerable resources. This paper presents a generic software framework that reduces development time through abstract classes and software reuse, and more importantly, aids design with support of our user-defined strategies and hybridization of meta-heuristics. Most interestingly, we propose a novel way of redefining hybridization with the use of the "request and response" metaphor, which form an abstract concept for hybridization. Different hybridization schemes can now be formed with minimal coding, which gives our proposed Meta-heuristics Development Framework its uniqueness. To illustrate the concept, we restrict to two popular meta-heuristics Ant Colony Optimization and Tabu Search, and demonstrate MDF through the implementation of various hybridized models to solve the Traveling Salesman Problem.