Self-exciting vibration of mills with constant or rising amplitude is called chatter vibration. Chatter limits the rolling speed, degrades product quality, and sometimes damages the mill. Therefore optimization of the rolling parameters to avoid the chatter is of great importance. In this paper a new introduced parameter, i.e., system equivalent damping (SED) is employed to determine the optimum rolling condition of a three stands mill. For any sand, SED presents a quantitative measure that shows mill tendency for chattering. The SED parameter is introduced on part I of this research by the authors. A SED value less than zero means that chatter occurs. Although the optimization problem for a complete tandem rolling mill involves several parameters, the design parameters on this research are limited to nine, where the reduction ratio and friction coefficient in each stand, strip speed, shear yield point of the strip material, and strip width are chosen as the design parameters. These parameters are the most important input parameters for an existing plant. The goal is to maximize the value of the SED parameter. The mathematical modeling of the rolling process with these nine design parameters leads to a set of nonlinear equations without an analytical solution and so cannot be applied to supervision systems. Therefore for the optimization process a database is prepared with the aforementioned nine input parameters. The database table is prepared using a computer code based on the Hu and Ehmann work, that is explained in Part I. The method presented here has the following three steps: Taguchi’s method in design of experiments (T-DOE), an artificial neural network (ANN), and a genetic algorithm (GA). The T-DOE reduces the number of required simulations tremendously, in other words, it reduces the number of the rows in the database table. The Taguchi’s L64 array is used in this regard. The ANN is used as an interpolation function between the input data in the database table. This function is able to cover the rolling conditions for whole of the products of the plant. Finally, using the genetic algorithm, a constrained optimization problem is solved in order to find the optimum rolling condition. In the optimization step the customer's desired characteristics of the product like; strip total reduction, strip width and strip material are taken out from the input parameters and the remaining are defined as the design parameters.