E-commerce websites host extensive and heterogeneous web entities requiring constant structural modifications to generate optimal online storefronts in alignment with the latest trends, market changes, customer needs, and expectations. Such website restructuring problems require the generation of optimal web entity sequences where the order and placement of web entities maximizes the sales even while facilitating a lasting online customer relationship. Most existing research in designing such websites focuses on making superficial changes, such as addition of new and popular links or automatic creation of index pages to ease navigation. Moreover, the existing research did not focus much on display time, aesthetic appeal, location, size, discounts, customer preferences, and ongoing trends, etc. taken together, while restructuring websites. In this paper, these factors have been taken into consideration by the proposed novel transformation-based adaptive website structure optimization model for e-commerce websites. Further, genetic algorithm has been used in the proposed model to compute optimal web entity sequences. Experimental results showed that the web entity sequences computed using the proposed model results in effective and user-friendly website design that would foster a lasting customer relationship.