This paper describes an approach to constructing derivative-free parallel algorithms for unconstrained optimization which are easy to implement on parallel machines. A special feature of this approach is the ease with which algorithms can be generated to take advantage of any number of processors and to adapt to any cost ratio of communication to function evaluation. The algorithms given here are supported by a strong convergence theorem, promising computational results, and an intuitively appealing interpretation as multi-directional line search methods.