The Directed Energy Deposition (DED) process, a subset of Additive Manufacturing (AM), presents significant opportunities for the production and repair of complex metal components [1]. However, challenges such as overheating can lead to defects and compromised part quality [2]. This study investigates the use of simulation-based optimization, leveraging Python and Abaqus thermal simulations, to address these challenges. Methodology Key parameters of the DED process, including laser power and waiting times, were optimized to maintain a suitable temperature range during printing. An iterative simulation approach was employed, demonstrating substantial reductions in peak temperatures through the scaling of laser power and the addition of waiting throughout the process. Validation and Results The methodology was validated with a test print that was monitored with an IR camera. This highlighted the positive impact of laser power adjustments on overheating to non-optimized methods. These findings underscore the potential of simulation-driven parameter optimization in enhancing DED print reliability, precision, and quality, particularly in industries such as aerospace. Conclusion and Future Directions This research contributes to the advancement of DED 3D printing as a robust and scalable manufacturing solution. Future steps will involve refining the optimization script, conducting additional physical validation tests on complex geometries, and addressing challenges such as varying wall heights and thicknesses resulting from laser power adjustments. By reducing trial-and-error in physical testing and enabling more efficient and cost-effective manufacturing, this methodology paves the way for improved DED process reliability and part quality.