Aesthetic Image Cropping (AIC) aims to improve the visual appeal of images by removing redundant content while preserving attractive elements. Despite the encouraging progresses achieved in data-driven approaches, most existing models struggle to understand user intentions, particularly for diversified scenes with multiple subjects. Moreover, they can only provide cropping results without explanations, which further restricts their usability in real-world applications. Motivated by the above facts, we introduce InstructCrop : a multimodal large language model (MLLM)-based AIC framework, which can understand user instructions and provide explanatory reasons for cropping results. Specifically, we first build a multimodal Image Cropping Instruction Tuning (ICIT) dataset through a cost-effective paradigm by generating high-quality instruction tuning data based on the existing cropping datasets. Then, we embed dynamic domain knowledge into the cropping model by integrating cropping-aware experts of aesthetic assessment and composition classification. Finally, we adapt MLLMs to generate the cropping results and corresponding explanations. Quantitative and qualitative experiments on three benchmark datasets demonstrate that InstructCrop enables effective and interpretable image cropping, which aligns better with user intentions. Data and code are available at https://github.com/sxfly99/InstructCrop.