The furnace temperature profile serves as the cornerstone of the reflow oven process in surface mount technology, and optimizing the furnace temperature curve is vital for achieving both high efficiency and quality in reflow oven production. Drawing upon Newton’s law of cooling, a model is formulated to determine the maximum conveyor belt speed through the oven, thereby enhancing the controllability of the welding process and stabilizing production parameters for superior reflow oven quality. Furthermore, process boundaries are parameterized, and a multi-objective optimization model of heating factors is established to design a temperature control system for the reflow oven, ensuring high production efficiency. By employing a constrained quantification method and introducing updated heating factors, the problem is transformed into a single-objective optimization task. Genetic algorithms are then utilized to optimize the results, precisely tuning parameters such as the temperature in each zone and the conveyor belt speed. Experimental findings indicate that the above optimization method can ensure the production efficiency of the reflow oven while achieving lower system costs and superior product quality. The proposed optimization method provides novel insights and references for the efficient operation of reflow ovens.