Several Differential Evolution (DE) alternatives have been suggested to address complex optimization challenges. However, these alternatives have shortcomings, including slow convergence speed and improper population diversity. The DE’s performance highly depends upon mutation schemes and its associative control parameters values (scaling factor and crossover probability). Thus, to overcome such inconveniences, this study suggested a Revised Differential Evolution (ReDE) algorithm to tackle engineering optimization. In ReDE, incorporated a novel mutation strategy to uphold search efficacy, a new control parameter to balance exploration and exploitation capability, and an innovative crossover possibility to sustain diversification. Efficacy of the proposed ReDE is evaluated on ten test problems, including unimodal (UM), multimodal (MM), and fixed dimensional (FD) functions, and subsequently applied to unconstrained optimization challenges (gear train design problem (GTD)). Experimental results compared with numerous popular, recent and high-performance optimizers. It demonstrates that ReDE is highly effective (due to better search efficiency, exploration and exploitation ability, and proper diversity) in discovering the optimal or near-optimal solutions more quickly.