  Ground-based observing time is precious in the era of exoplanet follow-up and
characterization, especially in high-precision radial velocity instruments.
Blind-search radial velocity surveys thus require a dedicated observational
strategy in order to optimize the observing time, which is particularly crucial
for the detection of small rocky worlds at large orbital periods. We develop an
algorithm with the purpose of improving the efficiency of radial velocity
observations in the context of exoplanet searches, and we apply it to the
K-dwarfs Orbited By habitable Exoplanets (KOBE) experiment. We aim at
accelerating exoplanet confirmations or, alternatively, rejecting false signals
as early as possible in order to save telescope time and increase the
efficiency of both blind-search surveys and follow-up of transiting candidates.
Once a minimum initial number of radial velocity datapoints is reached in such
a way that a periodicity starts to emerge according to generalized Lomb-Scargle
(GLS) periodograms, that period is targeted with the proposed algorithm, named
$\texttt{KOBEsim}$. The algorithm selects the next observing date that
maximizes the Bayesian evidence for such periodicity in comparison with a model
with no Keplerian orbits. By means of simulated data, we prove that the
algorithm accelerates the exoplanet detection, needing $29 - 33\,\%$ less
observations and $41 - 47\,\%$ less timespan of the full dataset for low-mass
planets ($m_{\rm p}\,<\,10\,M_{\oplus}$) in comparison with a conventional
monotonic cadence strategy. The enhancement in the number of datapoints for
$20\,M_{\oplus}$ planets is also appreciable, $16\,\%$. We also test
$\texttt{KOBEsim}$ with real data for a particular KOBE target, and for the
confirmed planet $HD~102365\,b$. Both of them demonstrate that the strategy is
capable of speeding up the detection up to a factor of $2$.
