Individuals with intellectual disabilities often have difficulties in comprehending complex texts. While many text-to-image models prioritize photorealism over cognitive accessibility, it is not clear how visual illustrations relate to text simplifications (TS) generated from them. This paper presents a structured vision language model (VLM) prompting framework for generating cognitively accessible images from simplified texts. We designed five prompt templates, i.e., Basic Object Focus, Contextual Scene, Educational Layout, Multi-Level Detail, and Grid Layout, each following distinct spatial arrangements while adhering to accessibility constraints such as object count limits, spatial separation, and content restrictions. Using 400 sentence-level TS pairs from four established text simplification datasets (OneStopEnglish, SimPA, Wikipedia, ASSET), we conducted a two-phase evaluation: Phase 1 assessed template effectiveness with CLIP similarity scores, and Phase 2 involved expert annotation of generated images across ten visual styles by four accessibility specialists. Results show that the Basic Object Focus template achieved the highest semantic alignment, indicating that visual minimalism enhances accessibility. Expert evaluation further identified Retro style as the most accessible and Wikipedia as the most effective text source. Inter-annotator agreement varied across dimensions, with Text Simplicity showing strong reliability and Image Quality proving more subjective. Overall, our framework offers practical guidelines for accessible content creation and underscores the importance of structured prompting in AI-generated visual accessibility tools.