Multiobjective combinatorial problems are commonly encountered in practice and would benefit from the development of metaheuristics where the search effort is interactively guided towards the solutions favored by the decision maker. The present study introduces such an Interactive Genetic Algorithm designed for a general multiobjective combinatorial framework and discusses its behavior in simulations on the Multiobjective Knapsack Problem. The evolution strategies being employed reflect the multiobjective nature of the problem. The fitness of individuals in the population is estimated on the basis of preference information elicited from the decision maker, and continuously updated as the algorithm progresses. The presented results indicate that the algorithm performs well when simulated against decision makers with different underlying utility functions.