The best starting point to solve an optimization problem is seeking to understand the problem as much as possible. A better understanding of the problem’s characteristics offers two essential benefits. The first benefit is that it allows the selection of the most suitable tools for tackling the problem. The second benefit is that it allows better assessment of candidate solutions, which is of paramount importance when there are multiple solutions and there is not one that is clearly superior to the others. All this seems to be true for all kinds of problems, but it is certainly true for optimization problems with certain characteristics, such as the presence of noise in the evaluation of the goal function and the existence of conflicting optimization goals. This chapter discusses the characteristics that make an optimization problem harder to solve and why evolutionary algorithms are suitable tools for dealing with this kind of problem.