Effective personalization of web experiences constitutes matching the intent and interest of a user (or a group of users) to content they consume, while optimizing a set of target engagement metrics. With improved content consumption tracking via web analytics, such personalization is not only feasible but also valuable for a content publisher/owner with large volumes of content to choose from. However the multitude of media (desktop, mobile, etc.) and the diversity of users’ interests necessitates automation in this process of constructing personalized content experiences. In this paper, we propose a genetic algorithm based framework that chooses a subset of content items (from a large collection) that are relevant to a given user and determines their respective sizes and relative positions to construct a layout that is optimized for a chosen engagement metric. Comparisons against existing frameworks based on crowd-sourced annotations indicate improved prominence of key content (based on historic engagement metrics) by the proposed approach, while improving the information diversity of the content presented in the layout.