This paper presents a systematic study of **Generative Engine Optimization (GEO)** in the e-commerce sector, a practice now vital as LLMs deploy conversational shopping agents that rerank products. To address the lack of data and systematic methods in this emerging field, the authors introduce **E-GEO**, a novel benchmark dataset comprising over 7,000 realistic, intent-rich consumer queries paired with product listings. The research evaluates 15 existing content rewriting heuristics but finds that framing the process as an **optimization problem** yields much greater results. Specifically, an iterative prompt-optimization algorithm consistently delivers **superior ranking improvements** for products within the generative engine's output compared to relying on ad hoc rules. This successful systematic approach indicates the existence of a **stable, generally effective GEO strategy** that could be applied across various product domains.