1. A method for planning a semiconductor manufacturing process based on users' demands, comprising the steps of: establishing a genetic algorithm model and inputting data, wherein a fitness function of the genetic algorithm model is formed by adding a plurality of cost functions, setting each cost function to have the same weight, defining the number of genetic evolution generations, and defining the number of chromosomes generated by each generation; establishing a fuzzy system which has a plurality of input parameters and one output parameter, the output parameter representing percent difference of each cost function in neighbor generations; setting to have a modulation parameter corresponding to each input parameter for adjusting fuzzy sets of the output parameter; executing genetic algorithm actions; executing fuzzing actions, fuzzy inference actions, and defuzzing actions; eliminating the chromosomes that produces output parameter smaller than a defined lower limit, and defining the remaining chromosome that produce the largest output parameter as the optimum chromosome, wherein the genetic algorithm actions stops being executed upon the optimum chromosome; and determining whether or not the number of the defined number of the generations has been reached, if yes, executing the optimum chromosome of the last generation; if no, continuing executing the genetic algorithm actions.