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Applying SGA to practical large scale power networks reactive power optimization still existing problems like large searching space and time consuming. This paper advanced an improved genetic algorithm combining sensitivity analysis (IGACSA). The new algorithm combined sensitivity analysis to generate initial generation of individuals in stead the way of SGA. The crossover and mutation operation of SGA were improved in the IGACSA, the improved crossover operation in possession of the ability of fast local adjustment, the improved mutation operation combined sensitivity analysis to generate new individuals. Furthermore, IGACSA used sensitivity analysis to mini-adjust the result of IGA. In order to use IGACSA to fix on the capacity of new installed reactive power compensation equipments, two simple steps were adopted to suit for practical power system. In the end, applying the IGACSA to reactive power optimization for Shaoguan power network in Guangdong Province proved the algorithm proposed can cut down calculating time and achieve better results.