Optimizing NumPy performance involves leveraging built-in functions for efficient array operations. Key techniques include using vectorized operations, broadcasting, and the `axis` parameter for calculations. Utilizing `ufuncs`, appropriate data types, and in-place operations enhances speed and reduces memory usage. Avoid unnecessary copies to maintain performance.