This chapter extends geometric programming techniques to multiobjective settings. After defining multiobjective posynomial problems, it explores duality and solution methods adapted from single-objective cases. Applications highlight how geometric programming can handle nonlinear trade-offs in cost, output, and efficiency objectives. This includes problems such as the maximization of production and contemporaneous maximization of environmental quality, the model of a monopolistic firm that maximizes revenue and profit, and the nonlinear model of environmental control.