There is growing widespread adoption of augmented reality in tech-driven industries and sectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling, entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantum computing. However, ensuring smooth and intuitive interactions with augmented objects is challenging, requiring practical performance evaluation and optimisation models to assess and improve users' experiences as they engage with AR-enhanced devices or systems. In this paper, we adapt Fitts's Law and apply it to model and predict interaction difficulty with objects distributed across four spatial quadrants. We use genetic optimisation algorithms to fine-tune Fitts's Law parameters, achieving a model that significantly boosts predictive accuracy. Our optimised model demonstrates an approximately 40% reduction in interaction difficulty across all quadrants, leading to a more ergonomic and intuitive user interface. This study contributes to the Human-Computer Interaction (HCI) field by offering a refined metric for evaluating and optimising AR interfaces, addressing the unique challenges of three-dimensional interaction environments.