Targeted spectroscopic exoplanet surveys face the challenge of maximizing their planet detection rates by means of careful planning. The number of possible observation combinations for a large exoplanet survey, i.e., the sequence of observations night after night, both in total time and amount of targets, is enormous. Sophisticated scheduling tools and the improved understanding of the exoplanet population are employed to investigate an efficient and optimal way to plan the execution of observations. This is applied to the CARMENES instrument, which is an optical and infrared high-resolution spectrograph that has started a survey of about 300 M-dwarf stars in search for terrestrial exoplanets. We use evolutionary computation techniques to create an automatic scheduler that minimizes the idle periods of the telescope and that distributes the observations among all the targets using configurable criteria. We simulate the case of the CARMENES survey with a realistic sample of targets, and we estimate the efficiency of the planning tool both in terms of telescope operations and planet detection. Our scheduling simulations produce plans that use about 99$%$ of the available telescope time (including overheads) and optimally distribute the observations among the different targets. Under such conditions, and using current planet statistics, the optimized plan using this tool should allow the CARMENES survey to discover about 65$%$ of the planets with radial-velocity semi-amplitudes greater than 1$~mthinspace s^{-1}$ when considering only photon noise. The simulations using our scheduling tool show that it is possible to optimize the survey planning by minimizing idle instrument periods and fulfilling the science objectives in an efficient manner to maximize the scientific return.
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