Blackbox optimization typically arises when the functions defining the objective and constraints of an optimization problem are computed through a computer simulation. The blackbox is expensive to compute, can have limited precision and be contaminated with numerical noise. It may also fail to return a valid output, even when the input appears acceptable. Launching twice the simulation from the same input may produce different outputs. These unreliable properties are frequently encountered when dealing with real optimization problems. The term blackbox is used to indicate that the internal structure of the target problem, such as derivatives or their approximations, cannot be exploited as it may be unknown, hidden, unreliable or inexistent. There are situations where some structure such as bound or linear constraints may be exploited and in some cases, a surrogate of the problem is supplied or a model may be constructed and trusted. This chapter surveys algorithms for this class of problems, including a supporting convergence analysis based on the nonsmooth calculus. The chapter also lists numerous published applications of these methods to real optimization problems.