Optimal design of the bending sequence is a key link in sheet metal free bending sequence planning,and it has an important influence on simplifying operation and guaranteeing bending precision. Bending sequence must meet the requirements for not only no collision interference of the work piece and the mold,but also working efficiency and working precision,so bending point choice,molds select,turnover and turn round of sheet metal must be considered in each bending step. In this paper,a genetic algorithm is used to design bending sequence. The interference identification is used to determine coding,exchange and mutation of the genetic algorithm. The genetic algorithm is developed to calculate the current optimal feasible solution of the bending sequence,and then the influence of initial population and evolution generations of this method on the result is analyzed by example verifications. The results prove that a global optimal solution can be obtained while the bending point number was less than 10,and optimal bending sequence which is similar to the global optimal solution can be calculated while the bending point number was more than 10. The results converge gradually to the global optimal solution with the increase of the initial population and evolution generations. As to 18 points bending work-piece,with the initial population size 150 and the evolution generations 100,we can obtain the satisfying solution. Optimal design of the bending sequence is a key link in sheet metal free bending sequence planning, and it has an important influence on simplifying operation and guaranteeing bending precision. Bending sequence must meet the requirements for not only no collision interference of the work piece and the mold, but also working efficiency and working precision, so bending point choice, molds select, turnover and turn round sheet metal must be considered in each bending step. In this paper, a genetic algorithm is used to design bending sequence. is used to determine coding, exchange and mutation of the genetic algorithm. The genetic algorithm is developed to calculate the current optimal feasible solution of the bending sequence, and then the influence of initial population and evolution generations of this method on the result is analyzed by example verifications. The results prove that a global optimal solution can be obtained while the bending point number was less than 10, and optimal bending sequence which is similar to the global optimal solution can be calculated while the bending point number was more than 10. As results converge gradually to the global optimal solution with the increase of the initial population and evolution generations. As to 18 points bending work-piece, with the initial population size 150 and evolution generations 100, we can obtain the satisfying solution.