As three-dimensional (3D) art becomes increasingly prevalent in fields such as architecture, game design, and AR/VR, this study examines the use of multi-objective evolutionary algorithms (MOEAs) to generate diverse and aesthetically novel 3D art. While most existing research focuses on 2D art and single-objective 3D art generation, this study explores the effectiveness of combining multiple fitness measures to evolve abstract and complex 3D art. Six objective functions, representing high (Category 1) and low (Category 2) user-rated metrics, were paired in 60 unique combinations influenced by four directional factors. The research also analyses the impact of two MOEAs, NSGA-II and NSGA-III, using Hotelling’s \(T^2\) test to evaluate the diversity of the generated populations. The statistical test results showed that NSGA-III promotes greater diversity in the evolved 3D art when compared to NSGA-II, especially when combining specific fitness measures. Additionally, analysis of user ratings revealed that NSGA-II outperformed NSGA-III in generating models that resonated positively with users. The user rating also identified the most preferred fitness pairings, highlighting the subjective nature of aesthetic judgements. Finally, the research also examines the potential of large multimodal vision/language models to reduce subjectivity by exploring aesthetic appeal, structural complexity, and interpretive potential of the evolved 3D art. Interpretations from an open-source large multimodal model suggested that the evolved 3D art combined organic and geometric elements, resembling abstract representations of real-world objects and art styles. These findings suggest a promising avenue for using MOEAs to evolve abstract art and explore Large Language Models (LLMs)/multimodal LLMs as evolutionary operators to evolve abstract and aesthetic 3D art.