We present a novel computational model aimed at exploring the concept of developmental exaptations, a previously hypothesized phenomenon wherein existing aspects of a developmental process are reused for a new, beneficial function. We leverage computational simulations to study this due to the challenges of observing developmental exaptations directly in nature. Employing a special fitness landscape, our model simulates the evolution of developmental strategies, allowing the study of how certain developmental instructions, which were initially evolved for a different function (or no function), can be repurposed to enhance fitness. The model utilizes indirect encoding to mimic biological processes, facilitating the evolution of exaptations. We demonstrate how crucial mutations contribute to achieving global fitness maxima, indicative of developmental exaptations for our special landscape. Our results not only provide computational evidence for the plausibility of developmental exaptations, but also open avenues for further research into detecting and understanding these phenomena in more complex and dynamic environments. This work establishes a new approach for modeling and analyzing developmental exaptations.