Multiobjective process planning and scheduling (moPPS) is a most important, practical but very intractable problem in manufacturing systems. Many research works use multiobjective EA to solve such problems; however, they cannot achieve satisfactory results in both quality and speed. This paper proposes a fast and effective evolutionary algorithm (FEEA) to deal with moPPS problem. FEEA tactfully unites the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship-based (PDDR) fitness function. VEGA prefers the edge region of the Pareto front and PDDR has the tendency converging toward the central area of the Pareto front. These two mechanisms preserve both the convergence rate and the distribution performance. Numerical comparisons show that the convergence performance of FEEA is better than NSGA-II and SPEA2 while the distribution performance is slightly better or equivalent, and the efficiency is obviously better than they are.