The simultaneous exploration of tradeoffs between program memory, data memory and execution time requirements (3D) for DSP (digital signal processing) algorithms in embedded computing environments is a demanding application and example par excellence of a multi-objective optimization problem. In order to solve this problem, two evolutionary algorithms are shown to be successfully applicable for exploring Pareto-optimal solutions. For different well-known target DSP processors, the trade-off fronts are analyzed. The two approaches are quantitatively compared.