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Optimizing parameter configuration is an important way to optimize the performance of application server. The optimization strategy based on the traditional parameter adjustment is time-consuming and lacks of systematic and regularity. Based on the modular idea, the model of conditional attribute reduction can be constructed by using the target decision function to classify the parameters of the application server. The application server optimization algorithm based on attribute reduction, can remove parameters which are less important for the target decision function, and obtain the parameters of relative importance, so as to achieve the target focus adjustment, rapidly improve the performance of the system. Based on classical rough set theory, the existing reduction model optimization algorithm is constructed on the basis of equivalence relation, which is easy to cause a lot of damage and loss of information. This article through the expansion of equivalence relation to general two elements, using the generalized rough set theory to improve the application server optimization algorithm of modularization and attribute reduction model based on discernibility function are defined by the reduction of condition attributes, combined with the dependence of the calculation, obtain the final target parameters.