A genetic algorithm (GA)-based method is proposed to solve the nonlinear optimization problem of minimum zone cylindricity evaluation. First, the background of the problem is introduced. Then the mathematical model and the fitness function are derived from the mathematical definition of dimensioning and tolerancing principles. Thirdly with the least squares solution as the initial values, the whole implementation process of the algorithm is realized in which some key techniques, for example, variables representing, population initializing and such basic operations as selection, crossover and mutation, are discussed in detail. Finally, examples are quoted to verify the proposed algorithm. The computation results indicate that the GA-based optimization method performs well on cylindricity evaluation. The outstanding advantages conclude high accuracy, high efficiency and capabilities of solving complicated nonlinear and large space problems. A genetic algorithm (GA) -based method is proposed to solve the nonlinear optimization problem of minimum zone cylindricity evaluation. First, the background of the problem is introduced. Then the mathematical model and the fitness function are derived from the mathematical definition of dimensioning and tolerancing principles. Thirdly with the least squares solution as the initial values, the whole implementation process of the algorithm is realized in which some key techniques, for example, variables representing, population initializing and such basic operations as selection, crossover and mutation, are discussed in detail. Finally, examples are quoted to verify the proposed algorithm. The computation results that that well on cylindricity evaluation. The ultimate advantages conclude high accuracy, high efficiency and capabilities of solving complicated nonlinear and large space problems .