The effect of diffuser-enhanced flow on the energy extraction performance of hydrokinetic turbines is widely reported in the literature. In this context, this work combines automatic scripting for geometry construction and meshing, together with a simulation-based approach using particle-swarm optimization with Computational Fluid Dynamics (CFD) to design an augmented hydrokinetic river turbine (HKRT). The goal is to achieve an increase in mass flow in the throat of the diffuser and then to design an ad-hoc set of rotor blades. The optimization is performed in two separate stages: Continuous optimization for the diffuser and discrete optimization for the rotor blades. The complex shape of the rotor blades in terms of the chord, twist, and thickness distribution was parametrized with second-order Bezier curves, which facilitates the correlation between the blade’s shape and its performance, proving to be more intuitive to the experienced designer. Furthermore, a novel search and discrete arrangement of feasible design variables are proposed, which allows for reducing the computational costs of this large optimization problem. Results show that the optimized diffuser achieved a flow rate increase of 279%, and the ad-hoc 1.5 [m] in diameter rotor delivers a power of 11.98 [kW] with an efficiency of 0.3385%.