Protein ligand charge can impact physiological delivery with charge reduction often benefiting performance. Yet neutralizing mutations can be detrimental to protein function. Herein, three approaches are evaluated to introduce charged-to-neutral mutations of three cations and three anions within an affibody engineered to bind epidermal growth factor receptor. These approaches – combinatorial library sorting or consensus design, based on natural homologs or library-sorted mutants – are used to identify mutations with favorable affinity, stability, and recombinant yield. Consensus design, based on 942 affibody homologs, yielded a mutant of modest function (Kd = 11 ±4 nM, Tm = 62 °C, and yield = 4.0 ±0.8 mg/L as compared to 5.3 ±1.7 nM, 71 °C, and 3.5 ±0.3 mg/L for the parental affibody). Extension of consensus design to ten additional mutants exhibited varied performance including a substantially improved mutant (Kd = 6.9 ±1.4 nM, Tm = 71 °C, and 12.7 ±0.9 mg/L yield). Sorting a homolog-based combinatorial library of 7×105 mutants generated a distribution of mutants with lower stability and yield, but did identify one strongly binding variant (Kd = 1.2 ±0.3 nM, Tm = 69 °C, and 6.0 ±0.4 mg/L yield). Synthetic consensus design, based on the amino acid distribution in functional library mutants, yielded higher affinities (p=0.05) with comparable stabilities and yields. The best of four analyzed clones had Kd = 1.7 ±0.5 nM, Tm = 68 °C, and 7.0 ±0.5 mg/L yield. While all three approaches were effective in creating targeted affibodies with six charged-to-neutral mutations, synthetic consensus design proved to be the most robust. Synthetic consensus design provides a valuable tool for ligand engineering, particularly in the context of charge manipulation.