Servomotor uses feedback controller to control either the speed or the position or both. This paper disucses the performance comparisons of a modified genetic algorithm, named as the semi- paraller operation genetic algorithm (SPOGA) and the conventional genetic algorithm (GA), in optimizing the I/O scale factors, membership functions, and rules of a hybrid-fuzzy controller, namely fuzzy-logic pararellel integral controrell (FLIC). Singleton fuzzification is used a fuzzifer with seven membership functions for both input and output of the controller, whisilt center of average is used as a defuzzifier. A21-bit-30-population is used in SPOGA for both I/O scales and for membership functions. Two control modes are applied in cascaded: position control and speed control. Both the simulation and practial experiment results show that FLIC with SPOGA-optimezed is better as compared to FLIC with GA-optimazed and also the non-optimazed FLIC, FLC, and PI in terms of perfomance and the reduction of the number of test runs during the optimization process. Keyword-component; FLIC, Genetic algorithm, servomotor, I/O scales and parameters, SPOGA