For heterogeneous multi-core architectures, efficient development of parallel software is paramount. Fast and accurate compiler technology is required in order to exploit their advantages and to optimise for multiple objectives, such as performance and power. The work at hand presents a heuristic and state-of-the-art Evolutionary Multi Objective Algorithm (EMOA) approach to tackle this problem. The performance and consistency of the population based heuristic TONPET and the indicator based EMOA are compared and thoroughly analysed. For the evaluation, both are integrated into the SLX tool suite. Representative benchmarks and three different MPSoC platforms are chosen for an in-depth realistic analysis. For smaller and medium sized solution spaces, TONPET outperforms the EMOA with 4.7% better Pareto fronts on average, while being 18 × faster in the worst case. In vast solution spaces, the EMOA consistently produces 3% better Pareto fronts on average but TONPET runs 88 × faster in the worst case. Furthermore, for comparison purposes, a full performance consistency analysis on EMOA conducted.