Many statistical facts are conveyed through charts.While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools.Recently, the advent of image-generating Large Language Models has facilitated chart generation; however, even this process often requires users to provide numerous constraints for accurate results.In this paper, we propose a loop-based framework for automatically evolving charts in a multi-agent environment.Within this framework, three distinct agents-Chart Code Generator, Chart Replier, and Chart Quality Evaluator-collaborate for iterative, user-tailored chart generation using large language models.Our approach demonstrates an improvement of up to 29.97% in performance compared to first generation, while also reducing generation time by up to 86.9% compared to manual promptbased methods, showcasing the effectiveness of this multi-agent collaboration in enhancing the quality and efficiency of chart generation.