The deployment of large language models (LLMs) across diverse cultural contexts has exposed the deficiencies of conventional alignment methods such as Reinforcement Learning from Human Feedback (RLHF), or Direct Preference Optimization (DPO) which typically demand extensive data collection and model training. To address these challenges, this study proposes an efficient and scalable cultural alignment strategy building on top of soft prompt tuning, a method that modifies input prompts while preserving the underlying model architecture. We use Differential Evolution (DE), a black-box optimization strategy, to effectively handle cases where a differentiable objective for cultural alignment cannot be formulated, ensuring consistency with cultural dimensions without the risk of overfitting.The proposed methodology not only minimizes the need for extensive data but also avoids model retraining, making it highly efficient. Our empirical findings indicate marked advancements in aligning LLM behavior within intricate cultural contexts, demonstrating the proposed method's practicality and effectiveness. This work contributes to closing the gap between computational models and the complexities of human culture, offering a significant step forward in the nuanced alignment of LLMs across diverse human contexts.