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Attribute reduction is an important aspect in data mining. In order to solve the reduction problems in a decision table with multiple attribute types, based on the theory of rough sets and binary relation aggregation, a two-phase genetic reduction algorithm is proposed using the attribute importance as evaluation criteria. The first stage of the algorithm is to find the reducts as much as possible, and the second stage is to find the minimal reduct. According to the goal of each stage, the coding scheme, the size of population, fitness function, termination condition, selection, mutation and correct operation of the two-phase genetic algorithm are designed. Experiments show that, compared with the standard genetic algorithm, the two-phase algorithm is more accurate and stable in the calculation of reduct. [ABSTRACT FROM AUTHOR]