In real-world multi-objective problems, the evaluation of objective functions usually requires a large amount of computation time. Moreover, due to the curse of dimensionality, solving multiobjective problems often requires much longer computation time than solving single-objective problems. Therefore, it is essential to develop efficiency enhancement techniques for solving multi-objective problems. This paper investigates fitness inheritance as a way to speed up multiobjective genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results in two cases: fitness inheritance without fitness sharing and fitness inheritance with fitness sharing. Results show that the number of function evaluations can be reduced with the use of fitness inheritance.