Coal blending processes mainly use static and non-reactive blending methods like the well-known Chevron stacking. Although real-time quality measurement techniques such as online X-ray uorescence measurements are available, the possibility to explore a dynamic adaptation of the blending process to the current quality data obtained using these techniques has not been explored. A dynamic adaptation helps to mix the coal from different mines in an optimal way and deliver a homogeneous product. The paper formulates homogenization of coal in longitudinal blending beds as a bi-objective problem of minimizing the variance of the cross-sectional quality and minimizing the height variance of the coal heap in the blending bed. We propose a cone based evolutionary algorithm to explore different trade-off regions of the Pareto front. A pronounced knee region on the Pareto front is found and is investigated in detail using a knee search algorithm. There are many interesting problem insights that are gained by examining the solutions found in different regions. In addition, all the knee solutions outperform the traditional Chevron stacking method.