In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing when faced with complex element layout planning. To address this challenge, we introduce SEGA, a novel Stepwise Evolution paradigm for content-aware layout GenerAtion. Inspired by the systematic mode of human thinking, SEGA employs a hierarchical reasoning framework with a coarse-to-fine strategy: first, a coarse-level module roughly estimates the layout planning results; then, another refining module is leveraged to perform fine-level reasoning regarding the coarse planning results. Furthermore, we incorporate layout design principles as prior knowledge into the module to enhance its layout planning ability. Moreover, we present a new large-scale poster dataset, namely BIG-Poster with rich meta-information annotation. We conduct extensive experiments and obtain remarkable state-of-the-art performance improvement on multiple benchmark datasets.