This paper explores the application of evolutionary algorithms to enhance task generation for Neural Processes (NPs) in meta-learning. Meta-learning aims to develop models capable of rapid adaptation to new tasks with minimal data, a necessity in fields where data collection is costly or difficult. By integrating evolutionary strategies, we aim to enhance the efficiency and robustness of NPs. We evaluate our approach using 1-D function regression problems, where Genetic Algorithm generates diverse and challenging tasks. Our results show that the evolutionary approach improves learning efficiency and model performance, achieving lower Root Mean Squared Error (RMSE) compared to traditional methods.