Managing agricultural pests with an incomplete understanding of the impacts that tactics have on crops, pests, and other organisms poses risks for loss of short-term profits and longer-term negative impacts, such as evolved resistance and nontarget effects. This is especially relevant for the management of weeds that are viewed almost exclusively as major impediments to crop production. Seldom considered in weed management are the benefits weeds provide in agroecosystems, which should be considered for optimal decision-making. Integration of weed costs and benefits will become increasingly important as management for pests transitions away from nearly complete reliance on herbicides and transgenic crop traits as the predominant approach for control. Here, we introduce a weed-management decision framework that accounts for weed benefits and exemplify how in-crop weed occurrence can increase crop yields in which a highly damaging insect also occurs. We highlight a case study showing how management decision-making for common milkweed, which is currently controlled primarily with glyphosate in herbicide-tolerant corn, can be improved by integrating management of the European corn borer (ECB), which is currently controlled primarily by the transgenic toxin Cry1 in Bacillus thuringiensis corn. Our data reveal that milkweed plants harboring aphids provide a food source (honeydew) for parasitoid wasps, which attack ECB eggs. Especially at high ECB population densities (> 1 egg mass leaf–1), maintaining low milkweed densities (< 1 stem m–2), effectively helps to minimize yield losses from ECB and to increase the economic injury level of this aggressive perennial weed. In addition, milkweed is the host for the monarch butterfly, so breeding-ground occurrences of the plant, including crop fields, may help sustain populations of this iconic insect. Using a more-holistic approach to integrate the management of multiple crop pests has the capacity to improve decision-making at the field scale, which can improve outcomes at the landscape scale.