In many domains of embedded systems, the increasing performance demands are tackled by increasing performance capacity through the use of multicore technology. However, adding more processing units also introduces the issue of task allocation --- decisions have to be made which software task to run on which core in order to best utilize the hardware platform. In this paper, we present an optimization mechanism for allocating tasks to cores of a soft real-time embedded system, that aims to minimize end-to-end response times of task chains, while keeping the number of deadline misses below the desired limit. The optimization relies on a novel heuristic that proposes new allocation candidates based on information how tasks delay each other. The heuristic was evaluated in a series of experiments, which showed that it both finds better allocations, and does it in fewer iterations than two heuristics that we used for comparison.