This article introduces the formulation of the Virtual Machine Planning Problem in cloud computing systems. It deals with the effi- cient allocation of a set of virtual machine requests from customers into the available pre-booked resources the broker has in a number of cloud providers, maximizing the broker profit. Eight list scheduling heuristics are proposed to solve the problem, by taking into account different criteria for mapping request to available virtual machines. The experimental evaluation analyzes the profit, makespan, and flowtime results of the proposed methods over a set of 400 problem instances that account for realistic workloads and scenarios using real data from cloud providers.