A low-fidelity model using surrogate functions helps reduce the overall computational time for optimizing problems involving time-consuming evaluation of objectives and constraints. Most surrogate-assisted evolutionary multi- and many-objective optimization (SA-EM(a)O) algorithms evaluate a newly-created population member either using high-fidelity or low-fidelity models for all objectives and constraints. However, recent studies have shown that a mixed-fidelity evaluation of a population member, in which some objectives and constraints are evaluated using a high-fidelity model and others are evaluated using a low-fidelity model, may be more efficient. This is because the time saved by not evaluating constraint functions for largely infeasible or largely feasible solutions, and objective functions for largely infeasible solutions can be utilized to evaluate potential solutions near the constraint boundaries and feasible regions, respectively. In this paper, we propose a metric that determines the potential benefit for evaluating every objective and constraint function for every population member independently. The metric uses the potential for the solution’s probability to dominate other neighboring members, modeling error, and extent of constraint violation. We demonstrate the efficacy of the proposed approach by presenting results on two to 10-variable test and engineering design problems having up to eight objectives and up to 10 constraints. We compare the proposed approach with a SA-EMO algorithm which evaluates every constraint and objective function for every new offspring. This preliminary study shows promise as a new direction of research in the area of surrogate-assisted evolutionary multi- and many-objective optimization.