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The rapid advancement of 5G technology necessitates the development of efficient thermal management solutions to handle the increased heat dissipation demands of high-power electronic components. This study presents an optimization strategy for a microchannel cold plate designed for a prototype 5G front-end system, featuring four 22-Watt chips as heat sources. The cold plate, constructed from aluminum, incorporates multiple rectangular flow channels evenly spaced to facilitate uniform heat distribution, with an inlet runner. The primary objective of this study is to optimize the geometry of the flow channels and the coolant mass flow rate at the runner entrance to minimize entropy generation, thereby enhancing the heat dissipation capability of the cold plate while minimizing pressure drop. Given these challenges, this study aims to develop an optimization strategy for cold plate design. This research applies Bayesian Optimization (BO), and Response Surface Methodology (RSM) paired with Genetic Algorithm (GA), and FMINCON (sequential quadratic programming, a built-in optimizer of MATLAB). These methods are utilized to fine-tune the channel dimensions and coolant flow rate, and the data that is used to evaluate entropy of the system is obtained from conjugate heat transfer simulations solved by Ansys Fluent. By using the Gaussian Process model to build response surface and predicting function of entropy generation, the results indicate that BO outperforms RSM paired with GA and FMINCON in terms of entropy reduction with same number of samples.