A novel smooth achievement scalarizing function is introduced and its application in multiobjective optimization is explored. First, the finite maximal terms in the achievement scalarizing function are reformulated as a finite sum of the plus function max{x,0}, and based on the smoothing of the plus function max{x,0}, a new smooth achievement scalarizing function is developed. Second, the relationships between the solutions of the smoothing achievement scalarized problem and the (weakly) efficient solutions of the multiobjective optimization problem are studied. Finally, experimental results show that the smooth achievement scalarizing function method outperforms the traditional weighted linear scalarization method and the achievement scalarizing function method in terms of solution distribution uniformity and approximation quality on the Pareto front.