The input-referred noise (IRN) is one of the most crucial performance indicators for analog front-ends (AFEs) of neural recording devices. We present in this paper, a novel design approach for a low-noise amplifier (LNA) based on the transistor optimization method in CMOS technology. Because flicker noise is predominant in neural recording applications, the AFE has been designed so as to meet input-referred flicker noise specifications, whereas thermal noise contributions are monitored and controlled by flicker noise corner frequencies. Transistor optimization is accomplished by using a lookup table that encapsulates its performance based on its current density. Initially, transistors are optimized based on flicker noise performance; later, they may be further optimized based on their size, power consumption, transconductance, or thermal noise contribution. The proposed approach has been validated by designing a folded-cascode amplifier with an IRN ranging from 2 to 8 µV rms . The results of the simulation show that the errors of our design methodology are less than 10%, which is less than those of gm /I D and the inversion coefficient methods. The proposed LNA achieves 2.1 µV rms while consuming 0.83 µW from 1.2 V supply.