Over-consumption of services and products leads to potential natural resource exhaustion, high environmental impact, and societal inequalities. Individuals can achieve more sustainable consumption by drastically changing their lifestyle choices and potentially sacrificing personal values or wishes. Conversely, achieving sustainable consumption while accounting for personal values is a more complex challenge, as potential trade-offs arise when trying to satisfy sustainability and personal goals. This article focuses on the value-sensitive design of recommender systems with neural networks and genetic algorithms to support consumers to shop more sustainably while respecting their personal preferences. We formalize recommendations as a multi-objective optimization problem, where each objective represents different sustainability goals and personal values. Recommendations are generated and evaluated on a synthetic historical dataset based on real-world synthetic data sources. The results indicate considerable environmental impact, without extreme personal sacrifices when consumers accept only a fraction of the recommendations.