A two-objective aerodynamic optimization of a microscale ducted wind turbine was performed using a genetic algorithm. Two different fitness function pairs were considered for this purpose. In the first alternative the algorithm maximized the power produced while minimizing the drag force at a given wind speed and tip speed ratio. In the second alternative, however, the annual energy production was maximized while minimizing the maximum drag force developed between the cut-in and cut-off wind speeds. Computational fluid dynamics solutions performed for selected best designs showed that optimizations performed using the second alternative yielded better turbines, which could produce more power at lower drag. The best design of the second alternative was also observed to operate efficiently at a larger tip speed ratio range compared to the second alternative.