Open-pit mining blast patterns are designed to safely break rock mass at desired boundaries and to produce uniformly sized fragments that lead to the optimal performance of subsequent mining operations. An issue that is often overlooked, however, is the deviation of drillhole locations with respect to their coordinates as designed in the blasting plan. This causes changes in the spatial distribution of explosive energy; some areas of the blast may receive a surplus of explosive energy, while others see a reduction. This results in less uniform fragmentation, producing more fines and increasing chances of oversized boulders, which reduces the efficiency of loading, hauling, and crushing operations. Additionally, deviated drillholes can lead to slope stability issues, flyrock, and uneven benches. To mitigate these problems, this study attempts to reduce differences in the explosive energy distribution (EED) through an optimization approach that adjusts the height of explosive columns in blastholes. A case study combines planned drillhole coordinates of two blast patterns with both simulated deviations and actual deviated drillhole locations. The corresponding benches are discretized into block models, for which the explosive energy distribution is calculated from the planned drillhole configuration as well as the deviated holes. The optimization objective is then to minimize block-to-block differences in explosive energy by making discrete adjustments to the height of individual column charges. Since mathematical programming is not possible due to the complexity of the evaluated formula, solutions are generated by metaheuristic algorithms. Among the Genetic Algorithm (GA) and Tabu Search (TS), the latter is preferred for its local search neighborhood that gives superior computation times. An inconvenience of the block-to-block comparison is the introduction of very large differences in explosive energy in blocks nearest to deviated blastholes, which cannot be accounted for by any charge adjustments except reductions in the deviated hole itself. Because optimization should focus on manageable differences in lower-valued blocks, these high-valued blocks are discarded by applying a cap based on data percentiles. Obtained results show that optimized charging adjustments vary with the chosen percentile, which makes it challenging to define comprehensive truly optimal solutions. A bias for charge reductions at high percentiles gradually shifts towards a bias for charge increases at lower values. Because a bias-induced imbalance in the total amount of explosives (compared to the original charging) is undesirable, the setting closest to equilibrium is selected, which is the use of only the lower 50\% of data. In this configuration, optimization of the case study blast patterns results in improvements of the objective value of 0.53-1.54\%, or 2.14-3.94\% when only the blocks affected by the recommended charge adjustments are considered. The block-to-block comparison handles any interchangement of block values as unwanted differences, which should not always be the case. Therefore, the true improvements to the explosive energy distribution might be better expressed using an alternative optimization objective. Computation times could be further reduced by expanding on the developed algorithm, and combination with prior research would be helpful to include any GPS-related inaccuracies in the actual drillhole locations.