Abstract Recently, Sastry et al. [1] proposed a general procedure to estimate the cost of a minimal solution to a combinatorial optimization problem instance. Such an estimate can be used to determine termination criteria for general purpose combinatorial optimization techniques. We have extended this idea and designed an adaptive cooling schedule for simulated annealing and simulated sintering based on the cost of a minimal solution to a problem instance [2]. With our approach, a schedule of target-costs is determined based on the cost of an easily located solution and the cost (exact, estimated or lower-bound) of a minimal solution. For each target-cost, we search for a solution of cost less than or equal to the target-cost using a range of values for the control-parameter temperature. Two key attributes distinguish our approach from other cooling schedules. First, our schedule is goal-directed. Second, the value of temperature is allowed to increase as well as decrease. We call our schedule the extended goal-directed cooling schedule. Idealized placement (an abstraction of Symmetrical Array Field Programmable Gate Array placement) is used to illustrate the effectiveness of our cooling schedule. We compare the performance of our schedule to the performance of a well known schedule proposed by Huang [3]. In terms of terminal solution quality, we show that our schedule performs better than Huang's schedule, and that this difference in performance is statistically significant. In addition, we show that in terms of execution time, simulated sintering using the extended goal-directed schedule performs better than simulated annealing using the extended goal-directed schedule without incurring a statistically significant penalty in terminal solution quality.